CS 7:
Personal Finance for Engineers
Introduction to the fundamentals and analysis specifically needed by engineers to make informed and intelligent financial decisions. Course will focus on actual industry-based financial information from technology companies and realistic financial issues. Topics include: behavioral finance, budgeting, debt, compensation, stock options, investing and real estate. No prior finance or economics experience required.
Terms: Aut
| Units: 1
CS 11SI:
How to Make VR: Introduction to Virtual Reality Design and Development
In this hands-on, experiential course, students will design and develop virtual reality applications. You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. The class will teach the design best practices and the creation pipeline for VR applications. Students will work in groups to present a final project in building an application for the Oculus Quest 2 headset. Enrollment is limited and by application only. See https://cs11si.stanford.edu for more information and the link to the application. Prerequisite: CS 106A or equivalent
Terms: Aut
| Units: 2
CS 12SI:
Spatial Computing Workshop
This one-unit workshop introduces UX design fundamentals for XR (Extended Reality) applications through a combination of hands-on work sessions and guest lectures from industry and academic experts, focusing on spatial prototyping and introducing Xcode for implementing applications on the Apple Vision Pro. Prerequisite: CS 106A or equivalent basic coding experience. Please go to cs12si.stanford.edu for an application link.
Terms: Spr
| Units: 1
CS 21SI:
AI for Social Good
Students will learn about and apply cutting-edge artificial intelligence (AI) techniques to real-world social good spaces (such as healthcare, government, and environmental conservation). The class will balance high-level machine learning techniques? from the fields of deep learning, natural language processing, computer vision, and reinforcement learning? with real world case studies, inviting students to think critically about technical and ethical issues in the development and deployment of AI. The course structure alternates between instructional lectures and bi-weekly guest speakers at the forefront of technology for social good. Students will be given the chance to engage in a flexible combination of AI model building, discussion, and individual exploration. Special topics may include: tech ethics, human-centered AI, AI safety, education technology, mental health applications, AI in policy, assistive robotics. Prerequisites: programming experience at the level of CS106A. Application required for enrollment: http://tinyurl.com/cs21si2024. We encourage students from all disciplines and backgrounds to apply!
Terms: Spr
| Units: 2
CS 22A:
The Social & Economic Impact of Artificial Intelligence (INTLPOL 200, SYMSYS 122)
Recent advances in Generative Artificial Intelligence place us at the threshold of a unique turning point in human history. For the first time, we face the prospect that we are not the only generally intelligent entities, and indeed that we may be less capable than our own creations. As this remarkable new technology continues to advance, we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous and unpredictable machines raises many complex and troubling questions. How will society respond as they displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems are free of bias and align with human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? Are we merely a stepping-stone to a new form of non-biological life, or are we just getting better at building useful gadgets? The goal of this course is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of superintelligent machines. (Note: This course is pre-approved for credit at SLS and GSB. No programming or technical knowledge is required.)
Terms: Win
| Units: 1
CS 24:
Minds and Machines (LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200)
(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win, Sum
| Units: 4
| UG Reqs: GER:DB-SocSci, WAY-FR
CS 25:
Transformers United V4
Since their introduction in 2017, Transformers have taken the world by storm, and are finding applications all over Deep Learning. They have enabled the creation of powerful language models like ChatGPT and Gemini, and are a critical component in other ML applications such as text-to-image and video generation (e.g. DALL-E and Sora). They have significantly elevated the capabilities and impact of Artificial Intelligence. In CS 25, which has become one of Stanford's hottest and most exciting seminars, we examine the details of how Transformers work, and dive deep into the different kinds of Transformers and how they're applied in various fields and applications. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. Potential topics include LLM architectures, creative use cases (e.g. art and music), healthcare/biology and neuroscience applications, robotics and RL (e.g. physical tasks, simulations, or games), and so forth. We invite folks at the forefront of Transformers research for talks, which will also be livestreamed and recorded through YouTube/Zoom. Past speakers have included Andrej Karpathy, Geoffrey Hinton, Jim Fan, Ashish Vaswani, and folks from OpenAI, Google DeepMind, NVIDIA, etc. Our class includes social events and networking sessions and has a popular reception within and outside Stanford, with around 1 million total views on YouTube. This is a 1-unit S/NC course, where attendance is the only homework! Please enroll on Axess or audit by joining the livestream (or in person if seats are available). Prerequisites: basic knowledge of Deep Learning (should understand attention) or CS224N/CS231N/CS230. Course website: https://web.stanford.edu/class/cs25/
Terms: Aut, Spr
| Units: 1
CS 29N:
Computational Decision Making
Although we make decisions every day, many people base their decisions on initial reactions or ""gut"" feelings. There are, however, powerful frameworks for making decisions more effectively based on computationally analyzing the choices available and their possible outcomes. In this course we give an introduction to some of these frameworks, including utility theory, decision analysis, and game theory. We also discuss why people sometimes make seemingly reasonable, yet irrational, decisions. We begin the class by presenting some of the basics of probability theory, which serves as the main mathematical foundation for the decision making frameworks we will subsequently present. Although we provide a mathematical/computational basis for the decision making frameworks we examine, we also seek to give intuitive (and sometimes counterintuitive) explanations for actual decision making behavior through in-class demonstrations. No prior experience with probability theory is needed (we'll cover what you need to know in class), but students should be comfortable with mathematical manipulation at the level of Math 20 or Math 41.
Terms: Win
| Units: 3
CS 40:
Cloud Infrastructure and Scalable Application Deployment
Trying to launch your next viral programming project and anticipating substantial user growth? This course will help you learn to implement your ideas in the cloud in a scalable, cost-effective manner. Topics will include cloud AI/ML pipelines, virtual machines, containers, basic networking, expressing infrastructure as code (IaC), data management, security and observability, and continuous integration and deployment (CI/CD). Through hands-on learning and practical examples, you'll learn to effectively deploy and manage cloud infrastructure. There is no out-of-pocket cost associated with this class and cloud credits will be provided for all students. Prerequisites: Programming maturity up to CS 107. Familiarity with the command line, version control, and basic development tools to the level of CS 45/CS 104, in particular: Basic Unix command line utilities and administration; Editing code with a TUI editor such as vim, emacs, or nano; Using Git and GitHub for collaborative projects (i.e. branching and pull requests); Basic familiarity with package managers for languages and operating systems (e.g., pip, apt, homebrew); Prior web development or networking experience helpful but not required.
Terms: Win
| Units: 3
CS 44N:
Great Ideas in Graphics
A hands-on interactive and fun exploration of great ideas from computer graphics. Motivated by graphics concepts, mathematical foundations and computer algorithms, students will explore an eccentric selection of "great ideas" through short weekly programming projects. Project topics will be selected from a diverse array of computer graphics concepts and historical elements.
Terms: Aut
| Units: 3
CS 51:
CS + Social Good Studio: Designing Social Impact Projects
Get real-world experience researching and developing your own social impact project! Students work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, community discussions, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Application required; please see cs51.stanford.edu for more information.
Terms: Win
| Units: 2
CS 52:
CS + Social Good Studio: Implementing Social Good Projects
Continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public. Prerequisite: CS 51, or consent of instructor.
Terms: Spr
| Units: 2
CS 53N:
How Can Generative AI Help Us Learn?
This seminar course will explore the science behind generative AI, the likely future of tools such as DALL-E, ChatGPT, GPT-4, and Bard, and the implications for education, both in and outside of structured school environments. Students in the course will work in teams to each become experts in some aspect of AI and in some way that generative AI could create a new future for education. The background for this course is the public release of ChatGPT, which created new awareness of the potential power of AI to dramatically change our lives. In considering the possible implications for education, ChatGPT has sparked dreams of automated personal tutors, customizable teaching assistance, AI-led collaborative learning, and revolutions in assessment. In addition to optimistic projections, there are clear and significant risks. For example, will AI-assisted learning be culturally appropriate and equally available to all? Can it increase opportunity for underprivileged learners worldwide, or will it accentuate privilege and privileged views? Will it help us learn faster, or distract us from thinking deeply about difficult problems ourselves? As experienced student learners, members of the class will be able to draw on their own educational history and design learning approaches that could change the future of their education and others in college or at other stages of their lives.
Terms: Spr
| Units: 3
CS 80E:
Dissecting The Modern Computer
In this course, students will be given a high-level, accessible introduction to computer architecture through the use of the RISC-V ISA. Through a series of interactive units, students will learn about the inner-workings of computers, from the execution of our programs all the way down to the hardware that runs them. Topics include simple digital circuits, assembly, simple processors, memory systems (Cache, DRAM, Disk), and bonus topics like GPU's. After completing this class, students should have a newfound appreciation for how incredible computational technology is, as well as direction to fantastic classes that delve into some of these topics in more detail, like CS149, EE108, and EE180. Prerequisite: CS106B.
Terms: Aut
| Units: 2
CS 83N:
Playback Theater
Playback combines elements of theater, community work and storytelling. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. A playback show brings about a powerful listening and sharing experience. During the course, we will tell, listen, play together, and train in playback techniques. We will write diaries to process our experience in the context of education and research. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. In playback, as in research, we are always moving together, from the known, to the unknown, and back. There is limited enrollment for this class. Application is required.
Terms: Win
| Units: 3
| UG Reqs: WAY-CE
CS 100ACE:
Problem-solving Lab for CS106A
Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.
Terms: Aut, Win, Spr
| Units: 1
CS 100BACE:
Problem-solving Lab for CS106B
Additional problem solving practice for the introductory CS course CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.
Terms: Aut, Win, Spr
| Units: 1
CS 103:
Mathematical Foundations of Computing
What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with CS103.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
Instructors: ;
Aiken, A. (PI);
Lian, Z. (PI);
Liu, A. (PI);
Schwarz, K. (PI);
Agashe, R. (TA);
Aiu, K. (TA);
Anvari, M. (TA);
Cao, S. (TA);
Carrell, T. (TA);
Chang, T. (TA);
Chung, J. (TA);
Han, R. (TA);
Jee, Y. (TA);
Kung, B. (TA);
Pandya, D. (TA);
Raman, V. (TA);
Rodriguez, J. (TA);
Roman, E. (TA);
Saue- Fletcher, L. (TA);
Sotoudeh, M. (TA);
Xia, W. (TA);
Zhai, W. (TA)
CS 103ACE:
Mathematical Problem-solving Strategies
Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite: CS103.
Terms: Aut, Win, Spr
| Units: 1
CS 104:
Introduction to Essential Software Systems and Tools
Concepts that are prerequisites to many different CS classes, such as version control, debugging, and basic cryptography and networking, are either left for students to figure out on their own or are taught in "crash course" form on-the-fly during other, unrelated classes. We propose to develop a course that will teach students the skills necessary to be successful computer scientists, such as the command line, source code management and debugging, security and cryptography, containers and virtual machines, and cloud computing. In this course, students will both become proficient with practical tools and develop a deeper, intuitive understanding of the involved software systems and computer science concepts. With this deeper understanding, students can leverage critical thinking skills to intelligently and efficiently configure and troubleshoot software systems, assess the security and efficiency of particular tool usages, and synthesize new automation pipelines that integrate multiple tools. To summarize, instead of having just a cursory understanding of how to use these tools, students will learn how to most effectively use these tools to become proficient programmers and computer scientists. In addition, this course can provide a gentle introduction to potentially challenging computer science concepts (e.g., networking) that become a focus in subsequent courses and also help motivate some of the tool usages they will see later in the degree program.
Terms: Win
| Units: 3
CS 105:
Introduction to Computers
For non-technical majors. What computers are and how they work. Practical experience in development of websites and an introduction to programming. A survey of Internet technology and the basics of computer hardware. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Students with prior computer science experience at the level of 106 or above require consent of instructor. Prerequisite: minimal math skills.
Terms: Aut, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106A:
Programming Methodology
Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106AX:
Programming Methodologies in JavaScript and Python (Accelerated)
Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. This course targets an audience with prior programming experience, and that prior experience is leveraged so material can be covered in greater depth.
Terms: Aut
| Units: 3-5
| UG Reqs: WAY-FR
CS 106B:
Programming Abstractions
Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106E:
Exploration of Computing
This course, designed for the non-computer scientist, will provide students with a solid foundation in the concepts and terminology behind computers, the Internet, and software development. It will give you better understanding and insight when working with technology. It will be particularly useful to future managers and PMs who will work with or who will lead programmers and other tech workers. But it will be useful to anyone who wants a better understanding of tech concepts and terms. We'll start by covering the foundations of Computer Hardware, the CPU, Operating Systems, Computer Networks, and the Web. We will then use our foundation to explore a variety of tech-related topics including Computer Security (how computers are attacked and defensive measures that can be taken); Cloud Computing, Artificial Intelligence, Software Development, Human-Computer Interaction, and Computer Theory.nnPrerequisites: Some programming experience at the High School level of above will help students get the most out of the class, but the course can be successfully completed with no prerequisites.
Terms: Spr
| Units: 3
CS 106L:
Standard C++ Programming Laboratory
This class explores features of the C++ programming language beyond what's covered in CS106B. Topics include core C++ language features (e.g. const-correctness, operator overloading, templates, move semantics, and lambda expressions) and standard libraries (e.g. containers, algorithms, and smart pointers). Pre- or corequisite: CS106B or equivalent. Prerequisite: CS106B or equivalent. CS106L may be taken concurrently with CS106B.
Terms: Aut, Win, Spr
| Units: 1
CS 106M:
Enrichment Adventures in Programming Abstractions
This enrichment add-on is a companion course to CS106B to explore additional topics and go into further depth. Specific topics to be announced per-quarter; past topics have included search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and numerical recipes. Students must be co-enrolled in CS106B. Refer to cs106m.stanford.edu for more information.
Terms: Aut
| Units: 1
CS 106S:
Coding for Social Good
Survey course on applications of fundamental computer science concepts from CS 106B to problems in the social good space (such as health, trust & safety, government, security, education, and environment). Each week consists of in-class activities designed and delivered by student instructors. Introduces students to JavaScript and the basics of web development. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, the basics of open source software, and principles of cybersecurity. For more information, visit cs106s.stanford.edu. Pre/Corequisite: CS106B. Cardinal Course certified by the Haas Center for Public Service
Terms: Aut, Spr
| Units: 1
CS 107:
Computer Organization and Systems
Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization. Prerequisites: 106B or X, or consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: ;
Adekola, O. (PI);
Cain, J. (PI);
Gregg, C. (PI);
Keppler, A. (PI);
Troccoli, N. (PI);
Adekola, O. (TA);
Ahmed, K. (TA);
Andrews, S. (TA);
Angelika-Nikita, M. (TA);
Ascheman, A. (TA);
Aydin, A. (TA);
Ayoob, M. (TA);
Babbitt, L. (TA);
Borbon Miranda, C. (TA);
Cai, D. (TA);
Carrell, T. (TA);
Granado, M. (TA);
Gupta, A. (TA);
Jitaru, A. (TA);
Kanell, T. (TA);
Kaputa, Z. (TA);
Lee, M. (TA);
Rebelsky, D. (TA);
Rodriguez, S. (TA);
Tlemcani, R. (TA);
Vallabhaneni, H. (TA)
CS 107ACE:
Problem-solving Lab for CS107
Additional problem solving practice for the introductory CS course CS107. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 107 required.
Terms: Aut, Win, Spr
| Units: 1
CS 107E:
Computer Systems from the Ground Up
Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Enrollment limited to 40. Check website for details: http://cs107e.stanford.edu on student selection process. Prerequisite: CS106B or CS106X, and consent of instructor. There is a $75 course lab fee.
Terms: Win, Spr
| Units: 3-5
| UG Reqs: WAY-FR
CS 108:
Object-Oriented Systems Design
Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams. Prerequisite: 107.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 109:
Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
Instructors: ;
Cain, J. (PI);
Cochran, K. (PI);
Harvill, M. (PI);
Piech, C. (PI);
Alagbe, K. (TA);
Borbon Miranda, C. (TA);
Buch, D. (TA);
Chellah, O. (TA);
Cheng, K. (TA);
Cochran, K. (TA);
El Boudali, H. (TA);
Fakih, N. (TA);
Grover, K. (TA);
Gupta, A. (TA);
Harvill, M. (TA);
Kang, R. (TA);
Mejia, J. (TA);
Michel, I. (TA);
Palleti, R. (TA);
Qiu, T. (TA);
Ravi, N. (TA);
Tinker, J. (TA);
Vu, M. (TA);
Wang, S. (TA);
Woodrow, J. (TA);
Xie, M. (TA)
CS 109ACE:
Problem-solving Lab for CS109
Additional problem solving practice for the introductory CS course CS109. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Enrollment limited to 30 students, permission of instructor required. Concurrent enrollment in CS 109 required.
Terms: Aut, Win, Spr
| Units: 1
CS 111:
Operating Systems Principles
Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS110 that fulfills any requirement satisfied by CS110. Prerequisite: CS107.
Terms: Aut, Win, Spr
| Units: 3-5
Instructors: ;
Ousterhout, J. (PI);
Troccoli, N. (PI);
Ahmad-Stein, D. (TA);
Ayoob, M. (TA);
Baruah, N. (TA);
Cao, M. (TA);
Dange, R. (TA);
Escandon, E. (TA);
Gorelik, I. (TA);
Govil, Y. (TA);
Granado, M. (TA);
Hanlon, M. (TA);
Kansal, A. (TA);
Khandelwal, P. (TA);
Kohli, S. (TA);
Li, G. (TA);
Marchini, M. (TA);
Palleti, R. (TA);
Saracay, E. (TA);
Tang, E. (TA);
Verma, S. (TA);
Zhang, X. (TA);
Zuna Largo, W. (TA)
CS 111ACE:
Problem Solving Lab for CS111
Additional design and implementation problems to complement the material taught in CS111. In-class participation is required. Prerequisite: consent of instructor. Corequisite: CS111
Terms: Aut, Win, Spr
| Units: 1
CS 112:
Operating systems kernel implementation project
Students will learn the details of how operating systems work throughfour implementation projects in the Pintos operating system. Theprojects center around threads, processes, virtual memory, and filesystems. This class should not be taken by students who have taken orplan to take CS212 or CS140. Prerequisite: CS111 or permission of theinstructor.
Terms: Win
| Units: 3
CS 120:
Introduction to AI Safety (STS 10)
As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. We will study work in interpretability, robustness, and governance of AI systems - to name a few. Basic knowledge about machine learning helps but is not required. View the full syllabus at http://tinyurl.com/42rb2sfv. Enrollment is by application only. Apply online at https://forms.gle/v8msM8nJ5FgeEHx1A by 9:00 PM PDT on Saturday, March 16, 2024.
Terms: Spr
| Units: 3
CS 123:
A Hands-On Introduction to Building AI-Enabled Robots
This course offers a hands-on introduction to AI-powered robotics. Unlike most introductory robotics courses, students will learn essential robotics concepts by constructing a quadruped robot from scratch and training it to perform real-world tasks. The course covers a broad range of topics critical to robot learning, including motor control, forward and inverse kinematics, system identification, simulation, and reinforcement learning. Through weekly labs, students will construct a pair of tele-operated robot arms with haptic feedback, program a robot arm to learn self-movement, and ultimately create and program an agile robot quadruped named Pupper. In the final four weeks, students will undertake an open-ended project using Pupper as a platform, such as instructing it to walk using reinforcement learning, developing a vision system to allow Pupper to play fetch, or redesigning the hardware to enhance the robot's agility. Note: CS123 strives to achieve a balanced distribution of seniority across the undergrad student body. Within each seniority group, enrollment of students will follow a first-come-first-served approach. Please use the form below to enroll in the class. The form will be open on 9/1/2023 9:00AM Pacific Time. Please use this form to apply: https://docs.google.com/forms/d/e/1FAIpQLSdBSUqLjpD-a-GmwhPnRLMi7L1BMMzikl8yqwmQp-stMoDqIg/viewform
Terms: Aut
| Units: 3
CS 124:
From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).
Terms: Win
| Units: 3-4
| UG Reqs: WAY-AQR
Instructors: ;
Jurafsky, D. (PI);
Akintan, M. (TA);
Buch, D. (TA);
Gurusankar, G. (TA);
Kacharia, N. (TA);
Lee, H. (TA);
Leon, A. (TA);
McAvity, J. (TA);
Mukherjee, A. (TA);
Nabi, F. (TA);
Oyeniyi, T. (TA);
Phatak, U. (TA);
Ryan, M. (TA);
Santiago, F. (TA);
Wu, J. (TA);
Xiao, J. (TA)
CS 129:
Applied Machine Learning
(Previously numbered CS 229A.) You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as Math 51.
Terms: Win
| Units: 3-4
CS 131:
Computer Vision: Foundations and Applications
Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.
Terms: Win
| Units: 3-4
CS 137A:
Principles of Robot Autonomy I (AA 174A, EE 160A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3-4
CS 139:
Human-Centered AI
Artificial Intelligence technology can and must be guided by human concerns. The course examines how mental models and user models of AI systems are formed, and how that leads to user expectations. This informs a set of design guidelines for building AI systems that are trustworthy, understandable, fair, and beneficial. The course covers the impact of AI systems on the economy and everyday life, and ethical issues of collecting data and running systems, including respect for persons, beneficence, fairness and justice.
Terms: Spr
| Units: 3
CS 140E:
Operating systems design and implementation
Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.
Terms: Win
| Units: 3-4
CS 143:
Compilers
Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects. Prerequisites: 103 or 103B, 107 equivalent, or consent from instructor.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 144:
Introduction to Computer Networking
Principles and practice. Structure and components of computer networks, with focus on the Internet. Packet switching, layering, and routing. Transport and TCP: reliable delivery over an unreliable network, flow control, congestion control. Network names, addresses and ethernet switching. Includes significant programming component in C/C++; students build portions of the internet TCP/IP software. Prerequisite: CS110.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors: ;
Winstein, K. (PI);
Ahmed, K. (TA);
Cheruiyot, I. (TA);
Hanlon, M. (TA);
Husman, G. (TA);
Kim, J. (TA);
Kulkarni, T. (TA);
Miller, G. (TA);
Mitchell, Y. (TA);
Poole, R. (TA);
Uhegbu, K. (TA);
Wang, R. (TA)
CS 145:
Data Management and Data Systems
Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 147:
Introduction to Human-Computer Interaction Design
Introduces fundamental methods and principles for designing, implementing, and evaluating user interfaces. Topics: user-centered design, rapid prototyping, experimentation, direct manipulation, cognitive principles, visual design, social software, software tools. Learn by doing: work with a team on a quarter-long design project, supported by lectures, readings, and studios. Prerequisite: 106B or X or equivalent programming experience. Recommended that CS Majors have also taken one of 142, 193P, or 193A.nnPlease note: Less than 5 is only allowed for graduate students.
Terms: Aut
| Units: 3-5
CS 147L:
Cross-platform Mobile App Development
The fundamentals of cross-platform mobile application development with a focus on the React Native framework (RN). Primary focus on developing best practices in creating apps for both iOS and Android by using Javascript and existing web + mobile development paradigms. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Airbnb, Walmart, Tesla, and UberEats. Skills developed over the course will be consolidated by the completion of a final project. Required Prerequisites: CS106B.
Terms: Aut
| Units: 3
CS 148:
Introduction to Computer Graphics and Imaging
This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer generated images. The beginning of the course focuses on using Blender to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Then we move on to a more fundamental understanding of light and color, as well as how it impacts computer displays and printers. From this we discuss more thoroughly how light interacts with the environment, and we construct engineering models such as the BRDF and discuss various simplifications into more basic lighting and shading models. Finally, we discuss ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras in order to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class project consists of building out a ray tracer to create a visually compelling image. Starter codes and code bits will be provided here and there to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted towards in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged.This is the first course in the computer graphics sequence at Stanford. Topics include: Scanline Rendering; Triangles; Rasterization; Transformations; Shading; Triangle Meshes; Subdivision; Marching Cubes; Textures; Light; Color; Cameras; Displays; Tone Mapping; BRDF; Lighting Equation; Global Illumination; Radiosity; Ray Tracing; Acceleration Structures; Sampling; Antialiasing; Reflection; Transmission; Depth of Field; Motion Blur; Monte Carlo; Bidirectional Ray Tracing; Light Maps.
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-CE
Instructors: ;
Fedkiw, R. (PI);
Akintan, M. (TA);
Becker, N. (TA);
Frausto, J. (TA);
Halder, S. (TA);
He, H. (TA);
Jobalia, S. (TA);
Kaputa, Z. (TA);
Kuang, Z. (TA);
Leanza, L. (TA);
Li, J. (TA);
Li, K. (GP);
Mandal, U. (TA);
Omens, D. (TA);
Patterson, K. (TA);
Phan, K. (TA);
Rodriguez, S. (TA);
Ruban, R. (TA);
Shubhangi, S. (TA);
Tang, M. (TA);
Xia, J. (TA)
CS 149:
Parallel Computing
This course is an introduction to parallelism and parallel programming. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, MapReduce, Apache Spark, SPMD, message passing, SIMT, transactions, and nested parallelism). Significant parallel programming assignments will be given as homework. The course is open to students who have completed the introductory CS course sequence through 111.
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors: ;
Fatahalian, K. (PI);
Granado, M. (TA);
Guo, M. (TA);
Hong, J. (TA);
Hwa, J. (TA);
Jiang, S. (TA);
Kunjal, N. (TA);
Mitchell, Y. (TA);
Olukotun, O. (TA);
Shen, T. (TA);
Sundram, S. (TA);
You, Z. (TA)
CS 151:
Logic Programming
Logic Programming is a style of programming based on symbolic logic. In writing a logic program, the programmer describes the application area of the program (as a set of logical sentences) without reference to the internal data structures or operations of the system executing the program. In this regard, a logic program is more of a specification than an implementation; and logic programs are often called runnable specifications. This course introduces basic logic programming theory, current technology, and examples of common applications, notably deductive databases, logical spreadsheets, enterprise management, computational law, and game playing. Work in the course takes the form of readings and exercises, weekly programming assignments, and a term-long project. Prerequisite: CS 106B or equivalent.
Terms: Spr
| Units: 3
CS 152:
Trust and Safety (COMM 122, INTLPOL 267)
Trust and Safety is an emerging field of professional and academic effort to build technologies that allow people to positively use the internet while being safe from harm. This course provides an introduction to the ways online services are abused to cause real human harm and the potential social, operational, product, legal and engineering responses. Students will learn about fraud, account takeovers, the use of social media by terrorists, misinformation, child exploitation, harassment, bullying and self-harm. This will include studying both the technical and sociological roots of these harms and the ways various online providers have responded. The class is taught by a practitioner, a professor of communication, a political scientist, and supplemented by guest lecturers from tech companies and nonprofits. Cross-disciplinary teams of students will spend the quarter building a technical and policy solution to a real trust and safety challenge, which will include the application of AI technologies to detecting and stopping abuse. For those taking this course for CS credit, the prerequisite is CS106B or equivalent programming experience and this course fulfills the Technology in Society requirement. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material.
Terms: Spr
| Units: 3
CS 153:
Applied Security at Scale
This course is designed to help students understand the unique challenges of solving security problems at scale, and is taught by senior technology leaders from companies tackling hardware and software security for hundreds of millions of people. The course is split into six parts covering major themes: Basics, Confidential Computing, Privacy, Trust, Safety and Real World. The format of the class will include guest lectures from experts in each theme, covering a blend of both theory and real world scenarios. Prerequisite: CS110/CS111. Recommended but not required: CS155.
Terms: Win, Spr
| Units: 3
CS 154:
Introduction to the Theory of Computation
This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors: ;
Reingold, O. (PI);
Correa Dias Godoy, F. (TA);
DeMarco, D. (TA);
Horng, C. (TA);
Lee, O. (TA);
Mayer, T. (TA);
Miranda, N. (TA);
Patel, C. (TA);
Singh, J. (TA);
Venkatesh, R. (TA);
Weiner, O. (TA);
Yang, L. (TA)
CS 155:
Computer and Network Security
For juniors, seniors, and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable software. Prerequisite: 110. Recommended: basic Unix.
Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
CS 157:
Computational Logic
Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.
Terms: Aut
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 161:
Design and Analysis of Algorithms
Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 106B or 106X; 103 or 103B; 109 or STATS 116.
Terms: Aut, Win, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: ;
Anari, N. (PI);
Charikar, M. (PI);
Rubinstein, A. (PI);
Sotoudeh, M. (PI);
Conkey, A. (TA);
Cussen, H. (TA);
Dixit, A. (TA);
Garg, R. (TA);
Goyal, S. (TA);
Hosgur, E. (TA);
Ko, J. (TA);
Kolichala, P. (TA);
Li, W. (TA);
Liu, S. (TA);
Mayer, T. (TA);
Namboothiry, B. (TA);
Parada, R. (TA);
Patterson, K. (TA);
Rivkin, J. (TA);
Roghani, M. (TA);
Salahi, K. (TA);
Singh, A. (TA);
Singhal, A. (TA);
Stearns, C. (TA);
Wang, X. (TA);
Z. HaoChen, J. (TA);
Zhang, P. (TA);
Zhao, J. (TA)
CS 161ACE:
Problem-Solving Lab for CS161
Additional problem solving practice for CS161. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Concurrent enrollment in CS 161 required. Limited enrollment, permission of instructor, and application required.
Terms: Aut, Win
| Units: 1
CS 168:
The Modern Algorithmic Toolbox
This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.
Terms: Spr
| Units: 3-4
CS 170:
Stanford Laptop Orchestra: Composition, Coding, and Performance (MUSIC 128)
Classroom instantiation of the Stanford Laptop Orchestra (SLOrk) which includes public performances. An ensemble of more than 20 humans, laptops, controllers, and special speaker arrays designed to provide each computer-mediated instrument with its sonic identity and presence. Topics and activities include issues of composing for laptop orchestras, instrument design, sound synthesis, programming, and live performance. May be repeated four times for credit. Space is limited; see https://ccrma.stanford.edu/courses/128 for information about the application and enrollment process. May be repeat for credit
Terms: Spr
| Units: 1-5
| UG Reqs: WAY-CE
| Repeatable
4 times
(up to 20 units total)
CS 173A:
Foundations of Computational Human Genomics (BIOMEDIN 173A, DBIO 173A)
(Only one of 173A or 273A counts toward any CS degree program.) A coder's primer to Computational Biology through the most amazing "source code" known: your genome. Examine the major forces of genome "code development" - positive, negative and neutral selection. Learn about genome sequencing (discovering your source code from fragments); genome content: variables (genes), control-flow (gene regulation), run-time stacks (epigenomics) and memory leaks (repeats); personalized genomics and genetic disease (code bugs); genome editing (code injection); ultra conservation (unsolved mysteries) and code modifications behind amazing animal adaptations. Course includes primers on molecular biology and text processing. Prerequisites: comfortable coding in Python from the command line.
Terms: Win
| Units: 3-4
CS 177:
Human Centered Product Management
Ask any product person what the most important skills are for PMs and they'll say interpersonal dynamics-- negotiation, communication, conflict resolution, interviewing and more. This class will look at the role of product management through a human-centered lens, including customers and coworkers. As well, students will experience the Agile-Lean-UX development process. Course enrollment will be capped, an application will be sent out first day of class. Prerequisite: CS106A&B or equivalent. This class could be taken before or after 147.
Terms: Aut
| Units: 3-4
CS 181:
Computers, Ethics, and Public Policy
Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Prerequisite: CS106A. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see https://cs181.stanford.edu for more information.
Terms: Spr
| Units: 4
| UG Reqs: GER:EC-EthicReas, WAY-ER
CS 181W:
Computers, Ethics, and Public Policy (WIM)
Writing-intensive version of CS181. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, and Math/Comp Sci undergraduates. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see https://cs181.stanford.edu for more information.
Terms: Spr
| Units: 4
| UG Reqs: GER:EC-EthicReas, WAY-ER
CS 185:
Coding with LLM Assistants
In under a year, LLM assistants have become a tool that many professional software engineers can¿t imagine living without. In this course, we will explore that phenomenon and design curriculum and pedagogical adaptations to it. In this class, we will: Conduct a survey-based ethnography of how professional software engineers are using LLMs (e.g., do they find it more useful for architectural planning vs code creation vs code explanation vs identifying bugs; what percentage of the day are they using it; how comfortable do they feel using it to work in frameworks or languages they are themselves unfamiliar with, etc); Engage in structured exploration using different LLM coding assistant tools for actual Stanford assignments (in classes they¿ve already completed) and to perform new tasks in unfamiliar languages, and reflect on those experiences; Read what others are saying about the process of coding with LLMs through review of popular sources (e.g., podcasts, blog posts); Learn an overview of the science of teaching and learning, and what is needed for an effective education in software engineering; Design new curricular materials that address the new needs and practices of professional software engineers, using principles of good pedagogical design.
Terms: Aut
| Units: 2
CS 190:
Software Design Studio
This course teaches the art of software design: how to decompose large complex systems into classes that can be implemented and maintained easily. Topics include the causes of complexity, modular design, techniques for creating deep classes, minimizing the complexity associated with exceptions, in-code documentation, and name selection. The class involves significant system software implementation and uses an iterative approach consisting of implementation, review, and revision. The course is taught in a studio format with in-class discussions and code reviews in addition to lectures. Prerequisite: CS 140 or equivalent. Apply at: https://web.stanford.edu/class/cs190
Terms: Win
| Units: 3-4
CS 191:
Senior Project
Restricted to Computer Science students. Group or individual research projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include a research component, substantial programming, and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Public presentation of final application or research results is required. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdfhttps://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Angst, R. (PI);
Bailis, P. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Demszky, D. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McClelland, J. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Poldrack, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Rubin, D. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sosic, R. (PI);
Stamos, A. (PI);
Subramonyam, H. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 191W:
Writing Intensive Senior Research Project
Restricted to Computer Science students. Writing-intensive version of CS191. Register using instructor's section number. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf
Terms: Aut, Win, Spr
| Units: 3-6
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Demszky, D. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McClelland, J. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Saberi, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stamos, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 192:
Programming Service Project
Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-4
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Cain, J. (PI);
Cao, P. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Gregg, C. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Koller, D. (PI);
Kozyrakis, C. (PI);
Lam, M. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Wu, J. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zelenski, J. (PI);
Rutherford, E. (GP)
CS 193C:
Client-Side Internet Technologies
Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax. Prerequisite: programming experience at the level of CS106A.
Terms: Sum
| Units: 3
CS 193Q:
Introduction to Python Programming
CS193Q teaches basic Python programming with a similar end-condition to CS106AP: strings, lists, numbers, dicts, loops, logic, functions, testings, decomposition and style, and modules. CS193Q assumes knowledge of some programming language, and proceeds by showing how each common programming idea is expressed in Python. CS193Q moves very quickly, meeting 3 times for 4 hours for a total of 12 hours which is a mixture of lecture and lab time.
Terms: Aut
| Units: 1
CS 194:
Software Project
Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeated for credit. Prerequisites: CS109 and CS161.
Terms: Win, Spr
| Units: 3
| Repeatable
for credit
CS 194H:
User Interface Design Project
Advanced methods for designing, prototyping, and evaluating user interfaces to computing applications. Novel interface technology, advanced interface design methods, and prototyping tools. Substantial, quarter-long course project that will be presented in a public presentation. Prerequisites: CS 147, or permission of instructor.
Terms: Win
| Units: 3-4
CS 194W:
Software Project (WIM)
Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS194. Preference given to seniors. Prerequisites: CS109 and CS161.
Terms: Win, Spr
| Units: 3
CS 195:
Supervised Undergraduate Research
Directed research under faculty supervision. Register using instructor's section number. Students are required to submit a written report and give a public presentation on their work. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr
| Units: 3-4
| Repeatable
20 times
(up to 100 units total)
Instructors: ;
Achour, S. (PI);
Adeli, E. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Barrett, C. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Bohg, J. (PI);
Bouland, A. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Charikar, M. (PI);
Demszky, D. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Finn, C. (PI);
Follmer, S. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Goodman, N. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Ho, D. (PI);
Jurafsky, D. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Li, F. (PI);
Liu, K. (PI);
Manning, C. (PI);
McClelland, J. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Paepcke, A. (PI);
Pavone, M. (PI);
Piech, C. (PI);
Potts, C. (PI);
Re, C. (PI);
Savarese, S. (PI);
Troccoli, N. (PI);
Valiant, G. (PI);
Vitercik, E. (PI);
Wetzstein, G. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Zaharia, M. (PI)
CS 197:
Computer Science Research
An onramp for students interested in breaking new ground in the frontiers of computer science. Course format features faculty lectures introducing the fundamentals of computer science research, alongside special interest group meetings that provide mentorship and feedback on a real research project. Lecture topics include reading technical papers, practicing oral communication and technical writing skills, and independently formulating research questions. Any student may enroll for 4 units and select a research area (AI, HCI, Systems, etc.) for a quarter-long team programming project with a Ph.D. student mentor. Space may be limited. Prerequisite: CS106B.
Terms: Aut, Win, Spr
| Units: 3-4
CS 197C:
Computer Science Research: CURIS Internship Onramp
A version of CS 197 designed specifically for students who will be participating in spring/summer CURIS internships OR have an ongoing research project with a (Ph.D. student or professor) mentor in the Stanford Computer Science department. An onramp for students interested in breaking new ground in the frontiers of computer science. Course format features faculty lectures introducing the fundamentals of computer science research, alongside mentorship and feedback from the CURIS or research mentor on a real research project. Students will attend the same lectures as CS197 and may enroll for 3 units. Lecture topics include reading technical papers, practicing oral communication and technical writing skills, and independently formulating research questions. Students must have commitment from their CURIS or research mentor for weekly check-in meetings. Prerequisite: CS106B.
Terms: Spr
| Units: 3
CS 198:
Teaching Computer Science
Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://cs198.stanford.edu.
Terms: Aut, Win, Spr
| Units: 3-4
CS 198B:
Additional Topics in Teaching Computer Science
Students build on the teaching skills developed in CS198. Focus is on techniques used to teach topics covered in CS106B. Prerequisite: successful completion of CS198.
Terms: Aut, Win, Spr
| Units: 1
CS 199:
Independent Work
Special study under faculty direction, usually leading to a written report. Register using instructor's section number. Letter grade; if not appropriate, enroll in CS199P. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Adeli, E. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Anari, N. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Chang, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Demszky, D. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Fox, E. (PI);
Ganguli, S. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Grimes, A. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Ho, D. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Lin, H. (PI);
Liu, K. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Patrignani, M. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubin, D. (PI);
Rubinstein, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stanford, J. (PI);
Subramonyam, H. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yan, L. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 199P:
Independent Work
Special study under faculty direction, usually leading to a written report. Register using instructor's section number. CR/NC only, if not appropriate, enroll in CS199. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Angst, R. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Grimes, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Lin, H. (PI);
Liu, K. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Socher, R. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yan, L. (PI);
Yang, D. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 202:
Law for Computer Science Professionals
Businesses are built on ideas. Today's successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. Over the past 40 years, intellectual capital has emerged as the leading assets class. Ocean Tomo® estimates that over 80% of the market value of S&P 500 corporations now stems from intangible assets, which consist largely of intellectual property (IP) assets (e.g., the company and product names, logos and designs; patentable inventions; proprietary software and databases, and other proprietary product, manufacturing and marketing information). It is therefore vital for entrepreneurs and other business professionals to have a basic understanding of IP and how it is procured, protected, and exploited. This course provides an overview of the many and varied IP issues that students will confront during their careers. It is intended to be both informative and fun. Classes will cover the basics of patent, trademark, copyright, and trade secret law. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Emerging issues concerning the federal Computer Fraud & Abuse Act (CFAA) and hacking will be covered, as will employment issues, including employee proprietary information and invention assignment agreements, work made for hire agreements, confidentiality agreements, non-compete agreements and other potential post-employment restrictions. Recent notable lawsuits will be discussed, including Apple v. Samsung (patents), Alice Corp. v. CLS Bank (software and business method patents), Oracle v. Google (software/APIs), Waymo v. Uber (civil and criminal trade secret theft), and hiQ v. LinkedIn (CFAA). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Guest lectures typically include experts on open source software; legal and practical issues confronted by business founders; and, consulting and testifying as an expert in IP litigation. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise.
Terms: Spr
| Units: 1
CS 205L:
Continuous Mathematical Methods with an Emphasis on Machine Learning
A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. Written homework assignments and (straightforward) quizzes focus on various concepts; additionally, students can opt in to a series of programming assignments geared towards neural network creation, training, and inference. (Replaces CS205A, and satisfies all similar requirements.) Prerequisites: Math 51; Math104 or MATH113 or equivalent or comfort with the associated material.
Terms: Win
| Units: 3
Instructors: ;
Fedkiw, R. (PI);
Becker, N. (TA);
He, H. (TA);
Jobalia, S. (TA);
Kuang, Z. (TA);
Li, J. (TA);
Li, K. (TA);
Malde, R. (TA);
Omens, D. (TA);
Pradeep, G. (TA);
Uy, M. (TA);
Yan, B. (TA)
CS 206:
Exploring Computational Journalism (COMM 281)
This project-based course will explore the field of computational journalism, including the use of Data Science, Info Visualization, AI, and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech, and build trust. This course is repeatable for credit; enrollment priority given to students taking it for the first time.
Terms: Win
| Units: 3
| Repeatable
3 times
(up to 9 units total)
CS 210A:
Software Project Experience with Corporate Partners
Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS109 and CS161.
Terms: Win
| Units: 3-4
CS 210B:
Software Project Experience with Corporate Partners
Continuation of CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 210A
Terms: Spr
| Units: 3-4
CS 212:
Operating Systems and Systems Programming
Covers key concepts in computer systems through the lens of operatingsystem design and implementation. Topics include threads, scheduling,processes, virtual memory, synchronization, multi-core architectures,memory consistency, hardware atomics, memory allocators, linking, I/O,file systems, and virtual machines. Concepts are reinforced with fourkernel programming projects in the Pintos operating system. This classmay be taken as an accelerated single-class alternative to the CS111,CS112 sequence; conversely, the class should not be taken by studentswho have already taken CS111 or CS112.
Terms: Win
| Units: 3-5
CS 221:
Artificial Intelligence: Principles and Techniques
Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Terms: Aut, Spr
| Units: 3-4
Instructors: ;
Anari, N. (PI);
Charikar, M. (PI);
Koyejo, S. (PI);
Liang, P. (PI);
Phatak, U. (PI);
Sadigh, D. (PI);
Agarwal, R. (TA);
Bruzzese, T. (TA);
Camacho, S. (TA);
Dixit, A. (TA);
Frausto, J. (TA);
Hejna, J. (TA);
Hu, G. (TA);
Jeon, H. (TA);
Kansal, A. (TA);
Khurana, A. (TA);
Ko, J. (TA);
Lee, A. (TA);
Li, C. (TA);
Lin, K. (TA);
Liu, S. (TA);
Melo, L. (TA);
Michaels, J. (TA);
Nie, N. (TA);
Pampari, A. (TA);
Pandya, D. (TA);
Roman, E. (TA);
Ryan, M. (TA);
Taori, R. (TA);
Tchapmi, M. (TA);
Tchapmi P., L. (TA);
Verma, S. (TA);
Wang, R. (TA);
Wornow, M. (TA);
Yang, S. (TA);
Zhang, P. (TA);
Zhang, T. (TA)
CS 223A:
Introduction to Robotics (ME 320)
Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win
| Units: 3
CS 224C:
NLP for Computational Social Science
We live in an era where many aspects of our social interactions are recorded as textual data, from social media posts to medical and financial records. This course is about using a variety of techniques from machine learning and theories from social science to study human behaviors and important societal questions at scale. Topics will include methods for natural language processing and causal inference, and their applications to important societal questions around hate speech, misinformation, and social movements.
Terms: Spr
| Units: 3
CS 224G:
Apps With LLMs Inside
With ChatGPT, neural networks have had their Lisp moment. Conversation has become code and the model is the CPU for this ultimate programming language. A new universe of App development has opened up, and there are no guides for it, yet. This is a project course designed to explore the space of Apps built around LLMs, starting by playing with them, learning their limitations, and then applying a set of techniques to program them efficiently and effectively. Assignments are due on a two week "sprint" cadence to mimic a startup style environment. Guest lectures by area experts provide industry perspective.
Terms: Win
| Units: 3
CS 224N:
Natural Language Processing with Deep Learning (LINGUIST 284, SYMSYS 195N)
Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Terms: Win, Spr
| Units: 3-4
Instructors: ;
Hashimoto, T. (PI);
Manning, C. (PI);
Yang, D. (PI);
Agrawal, A. (TA);
Burns, K. (TA);
Chang, C. (TA);
Chang, J. (TA);
Chatterjee, S. (TA);
Chu, S. (TA);
Dai, T. (TA);
Ding, Z. (TA);
El Boudali, H. (TA);
Gao, Y. (TA);
Goldie, A. (TA);
Lee, A. (TA);
Lee, O. (TA);
Li, R. (TA);
Lim, D. (TA);
Liu, N. (TA);
Mahankali, A. (TA);
Nie, N. (TA);
Poole, R. (TA);
Salahi, K. (TA);
Sharma, A. (TA);
Singh, J. (TA);
Sriram, A. (TA);
Taori, R. (TA);
Verma, T. (TA);
Wang, A. (TA);
Wang, T. (TA);
Wu, J. (TA);
Yang, S. (TA);
Zhang, B. (TA);
Zhang, H. (TA);
Zhang, T. (TA);
Zhang, Y. (TA);
Ziems, C. (TA);
murty, S. (TA)
CS 224S:
Spoken Language Processing (LINGUIST 285)
Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites: CS124, CS221, CS224N, or CS229.
Terms: Spr
| Units: 2-4
CS 224V:
Conversational Virtual Assistants with Deep Learning
Generative AI, and in particular Large Language Models (LLMs), has already changed how we work and study. But this is just the beginning, as it has the potential of assisting and perhaps eventually automating knowledge workers in all areas, from law, medicine, to teaching and mental health therapists. This course will focus on the general principles and the latest research on methodologies and tools that can be applied to all domains. This is a project-oriented course, where students will gain hands-on experience in either methodology research or applying the concepts to create useful assistants for a domain of their choice. Topics include: (1) growing LLMs' knowledge through a combination of manual supervised learning and self-learning, (2) stopping LLMs from hallucination by grounding them with external corpora of knowledge, which is necessary for handling new, live, private as well as long-tail data, (3) handling external data corpora in different domains including structured and unstructured data, (4) experimentation and evaluation of conversational assistants based on LLMs, (5) controlling LLMs to achieve tasks, (6) persuasive LLMs, (7) multilingual assistants, and (8) combining voice and graphical interfaces. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, CS 224S, 224U.
Terms: Aut
| Units: 3-4
CS 224W:
Machine Learning with Graphs
Many complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling complex social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis. Prerequisites: CS109, any introductory course in Machine Learning.
Terms: Aut
| Units: 3-4
Instructors: ;
Leskovec, J. (PI);
Agrawal, A. (TA);
Ali Mohammadi, Y. (TA);
Garg, A. (TA);
Jin, M. (TA);
Li, Y. (TA);
Nilforoshan, H. (TA);
Oyeniyi, T. (TA);
Soni, P. (TA);
Sriram, A. (TA);
Zhang, X. (TA);
Zhu, C. (TA)
CS 225:
Machine Learning for Discrete Optimization (MS&E 236)
Machine learning has become a powerful tool for discrete optimization. This is because, in practice, we often have ample data about the application domain?data that can be used to optimize algorithmic performance, ranging from runtime to solution quality. This course covers how machine learning can be used within the discrete optimization pipeline from many perspectives, including how to design novel combinatorial algorithms with machine-learned modules and configure existing algorithms? parameters to optimize performance. Topics will include both applied machinery (such as graph neural networks, reinforcement learning, transformers, and LLMs) as well as theoretical tools for providing provable guarantees.
Terms: Spr
| Units: 3
CS 225A:
Experimental Robotics
Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. Prerequisites: 223A or equivalent.
Terms: Spr
| Units: 3
CS 227A:
Robot Perception: Hardware, Algorithm, and Application (EE 227)
Robot Perception is the cornerstone of modern robotics, enabling machines to interpret, understand, and respond to an array of sensory information they encounter. In the course, students will study the basic principles of typical sensor hardware on a robotics system (e.g., vision, tactile, and acoustic sensors), the algorithms that process the raw sensory data, and make actionable decisions from that information. Over the course of the semester, students will incrementally build their own vision-based robotics system in simulation via a series of homework coding assignments. Students enrolling 4 units will be required to submit an additional final written report. Prerequisites: This course requires programming experience in python as well as basic knowledge of linear algebra. Most of the required mathematical concepts will be reviewed, but it will be assumed that students have strong programming skills. All the homework requires extensive programming. Previous knowledge of robotics, machine learning or computer vision would be helpful but is not absolutely required.
Terms: Win
| Units: 3-4
CS 227B:
General Game Playing
A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr
| Units: 3
CS 228:
Probabilistic Graphical Models: Principles and Techniques
Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Win
| Units: 3-4
CS 229:
Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205.
Terms: Aut, Win, Sum
| Units: 3-4
Instructors: ;
Amjad, J. (PI);
Charikar, M. (PI);
Fox, E. (PI);
Guestrin, C. (PI);
Koyejo, S. (PI);
Ng, A. (PI);
Agarwal, R. (TA);
Agarwala, S. (TA);
Chang, C. (TA);
Chi, R. (TA);
Chow, W. (TA);
Chu, S. (TA);
Damiani, A. (TA);
Deng, R. (TA);
Desai, R. (TA);
Ding, Z. (TA);
Dong, K. (TA);
Frausto, J. (TA);
Jeon, H. (TA);
Khandelwal, P. (TA);
Kumbong, H. (TA);
Lim, D. (GP);
Schaeffer, R. (TA);
So, J. (TA);
Wang, A. (TA);
Wang, R. (TA);
Xiao, Z. (TA);
Yang, S. (TA);
Zhang, E. (TA)
CS 229B:
Machine Learning for Sequence Modeling (STATS 232)
Sequence data and time series are becoming increasingly ubiquitous in fields as diverse as bioinformatics, neuroscience, health, environmental monitoring, finance, speech recognition/generation, video processing, and natural language processing. Machine learning has become an indispensable tool for analyzing such data; in fact, sequence models lie at the heart of recent progress in AI like GPT3. This class integrates foundational concepts in time series analysis with modern machine learning methods for sequence modeling. Connections and key differences will be highlighted, as well as how grounding modern neural network approaches with traditional interpretations can enable powerful leaps forward. You will learn theoretical fundamentals, but the focus will be on gaining practical, hands-on experience with modern methods through real-world case studies. You will walk away with a broad and deep perspective of sequence modeling and key ways in which such data are not just 1D images.
Terms: Aut
| Units: 3-4
CS 229M:
Machine Learning Theory (STATS 214)
How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering this question. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. Prerequisites: linear algebra ( MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning ( CS 229, STATS 229, or STATS 315A).
Terms: Aut
| Units: 3
CS 229S:
Systems for Machine Learning
Deep learning and neural networks are being increasingly adopted across industries. They are now used to serve billions of users across applications such as search, knowledge discovery, and productivity assistants. As models become more capable and intelligent, this trend of large-scale adoption will continue to grow rapidly. Due to the widespread application, there is an increasing need to achieve high performance for both training and serving deep-learning models. However, performance is hindered by a multitude of infrastructure and lifecycle hurdles - the increasing complexity of the models, massive sizes of training and inference data, heterogeneity of the available accelerators and multi-node platforms, and diverse network properties. The slow adaptation of systems to new algorithms creates a bottleneck for the rapid evolution of deep-learning models and their applications. This course will cover systems approaches for improving the efficiency of machine learning pipelines - comprising data preparation, model training, and model deployment & inference -at each level of the systems stack spanning software and hardware.
Terms: Aut
| Units: 3
CS 231A:
Computer Vision: From 3D Perception to 3D Reconstruction and Beyond
(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.
Terms: Spr
| Units: 3-4
CS 231N:
Deep Learning for Computer Vision
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Prerequisites: Proficiency in Python - All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.College Calculus, Linear Algebra (e.g. MATH 19, MATH 51) -You should be comfortable taking derivatives and understanding matrix vector operations and notation. Basic Probability and Statistics (e.g. CS 109 or other stats course) -You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Terms: Spr
| Units: 3-4
Instructors: ;
Adeli, E. (PI);
Li, F. (PI);
Agarwala, S. (TA);
Durante, Z. (TA);
Gokmen, C. (TA);
Goyal, S. (TA);
Leanza, L. (TA);
Li, C. (TA);
Mukherjee, A. (TA);
Patel, C. (TA);
Ravi, N. (TA);
Xiang, T. (TA)
CS 233:
Geometric and Topological Data Analysis (CME 251)
Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Linear and non-linear dimensionality reduction techniques. Graph representations of data and spectral methods. The rudiments of computational topology and persistent homology on sampled spaces, with applications. Global and local geometry descriptors allowing for various kinds of invariances. Alignment, matching, and map/correspondence computation between geometric data sets. Annotation tools for geometric data. Geometric deep learning on graphs and sets. Function spaces and functional maps. Networks of data sets and joint learning for segmentation and labeling. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103.
Terms: Win
| Units: 3
CS 234:
Reinforcement Learning
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Prerequisites: proficiency in python, CS 229 or equivalents or permission of the instructor; linear algebra, basic probability.
Terms: Spr
| Units: 3
CS 235:
Computational Methods for Biomedical Image Analysis and Interpretation (BIOMEDIN 260, BMP 260, RAD 260)
The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.
Terms: Spr
| Units: 3-4
CS 236:
Deep Generative Models
Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning. Prerequisites: Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.
Terms: Aut
| Units: 3
Instructors: ;
Ermon, S. (PI);
Agarwal, P. (TA);
Cao, S. (TA);
Chatterjee, S. (TA);
Chen, H. (TA);
Chiang, B. (TA);
Dodhia, P. (TA);
He, J. (TA);
Miraoui, Y. (TA);
Obbad, E. (TA);
Salahi, K. (TA);
Sharma, D. (TA);
Shih, A. (TA);
Xiao, Z. (TA);
Xu, M. (TA);
Yang, C. (TA);
Yuan, S. (TA);
Zhou, L. (TA)
CS 237A:
Principles of Robot Autonomy I (AA 274A, EE 260A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3
CS 237B:
Principles of Robot Autonomy II (AA 174B, AA 274B, EE 260B)
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent, CME 100 or equivalent (for linear algebra), CME 106 or equivalent (for probability theory), and AA 171/274.
Terms: Win
| Units: 3-4
CS 238:
Decision Making under Uncertainty (AA 228)
This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. Prerequisites: basic probability and fluency in a high-level programming language.
Terms: Aut
| Units: 3-4
Instructors: ;
Kochenderfer, M. (PI);
Armour, G. (TA);
Asmar, D. (TA);
Delecki, H. (TA);
Jamgochian, A. (TA);
Kruse, L. (TA);
Lange, B. (TA);
Molins, E. (TA);
Schlichting, M. (TA);
Smyers, E. (TA);
Yang, J. (TA);
Yildiz, A. (TA)
CS 239:
Advanced Topics in Sequential Decision Making (AA 229)
Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites: AA 228/CS 238 or CS 221.
Terms: Win
| Units: 3-4
CS 240LX:
Advanced Systems Laboratory, Accelerated
This is an implementation-heavy, lab-based class that covers similar topics as CS240, but by writing code versus discussing papers. Our code will run "bare-metal" (without an operating system) on the widely-used ARM-based raspberry pi. Bare-metal lets us do interesting tricks without constantly fighting a lumbering, general-purpose OS that cannot get out of its own way. We will do ten projects, one per week, where each project covers two labs of (at a minimum) several hours each and a non-trivial amount of outside work. The workload is significant, but I will aim to not waste your time. Prerequisite: CS140E or instructor permission.
Terms: Spr
| Units: 3
CS 241:
Embedded Systems Workshop (EE 285)
Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of CS107, EE101A, EE108, ME80.
Terms: Win
| Units: 3
| Repeatable
3 times
(up to 9 units total)
CS 242:
Programming Languages
This course explores foundational models of computation, such as the lambda calculus and other small calculi, and the incorporation of basic advances in PL theory into modern programming languages such as Haskell and Rust. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, verification, and the semantic gap between computational models and modern hardware. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. Prerequisites: 103, 110.
Terms: Aut
| Units: 3-4
CS 243:
Program Analysis and Optimizations
Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection. Prerequisites: 103 or 103B, and 107.
Terms: Win
| Units: 3-4
CS 244:
Advanced Topics in Networking
Classic papers, new ideas, and research papers in networking. Architectural principles: why the Internet was designed this way? Congestion control. Wireless and mobility; software-defined networks (SDN) and network virtualization; content distribution networks; packet switching; data-center networks. Prerequisite: 144 or equivalent.
Terms: Spr
| Units: 3-4
CS 244B:
Distributed Systems
Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites: CS 144.
Terms: Spr
| Units: 3
CS 246:
Mining Massive Data Sets
The availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Prerequisites: At least one of CS107 or CS145.
Terms: Win
| Units: 3-4
| UG Reqs: WAY-FR
Instructors: ;
Leskovec, J. (PI);
Agrawal, A. (TA);
Allavarpu, E. (TA);
Garg, A. (TA);
He, B. (TA);
Jin, M. (TA);
Khurana, A. (TA);
Li, Y. (TA);
Liu, Y. (TA);
Siegel, S. (TA);
Zhu, C. (TA)
CS 247A:
Design for Artificial Intelligence (SYMSYS 195A)
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking. In the event of a waitlist, acceptance to class based on an application provided on the first day of class.
Terms: Aut
| Units: 3-4
CS 247B:
Design for Behavior Change (SYMSYS 195B)
Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS147 or equivalent.
Terms: Win
| Units: 3-4
CS 247G:
Design for Play (SYMSYS 195G)
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; please plan on attending every studio to take this class. The focus of CS247g is an introduction to theory and practice of game design. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. This class has multiple short projects, allowing us to cover a variety of genres, from narrative to pure strategy. Prerequisites: 147 or equivalent background.
Terms: Spr, Sum
| Units: 3-4
CS 247S:
Service Design (SYMSYS 195S)
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247S is Service Design. In this course we will be looking at experiences that address the needs of multiple types of stakeholders at different touchpoints - digital, physical, and everything in between. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. Let us explore what specialized tools and processes are required to created these multi-faceted interactions. Prerequisites: CS147 or equivalent background in design thinking. In the event of a waitlist, acceptance to class based on an application provided on the first day of class.
Terms: Win
| Units: 3-4
CS 248A:
Computer Graphics: Rendering, Geometry, and Image Manipulation
This course provides a comprehensive introduction to interactive computer graphics, focusing on fundamental concepts and techniques, as well as their cross-cutting relationship to multiple problem domains in interactive graphics (such as rendering, animation, geometry, image processing). Topics include: 2D and 3D drawing, sampling theory, interpolation, rasterization, image compositing, the real-time GPU graphics pipeline (and parallel rendering), VR rendering, geometric transformations, curves and surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, image processing, time integration, physically-based animation, and inverse kinematics. The course will involve several in-depth programming assignments and a self-selected final project that explores concepts covered in the class. Prerequisite: CS 107, MATH 51.
Terms: Win
| Units: 3-4
CS 248B:
Fundamentals of Computer Graphics: Animation and Simulation
This course provides a comprehensive introduction to computer graphics, focusing on fundamental concepts and techniques in Computer Animation and Physics Simulation. Topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, inverse kinematics, rigid body dynamics, deformable body simulation, and fluid simulation. Prerequisites: CS107 and MATH51.
Terms: Aut
| Units: 3
CS 249I:
The Modern Internet
Advanced networking course that covers how the Internet has evolved and operates today. Topics include modern Internet topology and routing practices, recently introduced network protocols, popular content delivery strategies, and pressing privacy, security, and abuse challenges. The course consists of a mixture of lecture, guest talks, and investigative projects where students will analyze how Internet operates in practice. Prerequisite: CS 144, EE 284, or equivalent.
Terms: Win
| Units: 3
CS 251:
Cryptocurrencies and blockchain technologies
For advanced undergraduates and for graduate students. The potential applications for Bitcoin-like technologies is enormous. The course will cover the technical aspects of cryptocurrencies, blockchain technologies, and distributed consensus. Students will learn how these systems work, and how to engineer secure software that interacts with Blockchains like Bitcoin, Ethereum, and others. Prerequisite: CS110. Recommended: CS255.
Terms: Aut
| Units: 3
CS 254:
Computational Complexity
An introduction to computational complexity theory. Topics include the P versus NP problem and other major challenges of complexity theory; Space complexity: Savitch's theorem and the Immerman-Szelepscényi theorem; P, NP, coNP, and the polynomial hierarchy; The power of randomness in computation; Non-uniform computation and circuit complexity; Interactive proofs. Prerequisites: 154 or equivalent; mathematical maturity.
Terms: Win
| Units: 3
CS 254B:
Computational Complexity II
A continuation of CS254 (Computational Complexity). Topics include Barriers to P versus NP; The relationship between time and space, and time-space tradeoffs for SAT; The hardness versus randomness paradigm; Average-case complexity; Fine-grained complexity; Current and new areas of complexity theory research. Prerequisite: CS254.
Terms: Spr
| Units: 3
CS 255:
Introduction to Cryptography
For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications. Prerequisite: basic probability theory.
Terms: Win
| Units: 3
CS 256:
Algorithmic Fairness
Machine learning and data analysis have enjoyed tremendous success in a broad range of domains. These advances hold the promise of great benefits to individuals, organizations and society. Undeniably, algorithms are informing decisions that reach ever more deeply into our lives, from news article recommendations to criminal sentencing decisions to healthcare diagnostics. This progress, however, raises (and is impeded by) a host of concerns regarding the societal impact of computation. A prominent concern is that these algorithms should be fair. Unfortunately, the hope that automated decision-making might be free of social biases is dashed on the data on which the algorithms are trained and the choices in their construction: left to their own devices, algorithms will propagate - even amplify - existing biases of the data, the programmers, and the decisions made in the choice of features to incorporate and measurements of 'fitness' to be applied. Addressing wrongful discrimination by algorithms is not only mandated by law and by ethics but is essential to maintaining the public trust in the current computation-driven revolution.The study of fairness is ancient and multi-disciplinary: philosophers, legal experts, economists, statisticians, social scientists and others have been concerned with fairness for as long as these fields have existed. Nevertheless, the scale of decision making in the age of big-data, the computational complexities of algorithmic decision making, and simple professional responsibility mandate that computer scientists contribute to this research endeavor. This is an intro to this booming area of research.Prerequisites: CS 161 and 221. I will be assuming some mathematical maturity, and CS 154 isrecommended.
Terms: Win
| Units: 3
CS 257:
Introduction to Automated Reasoning
Automated logical reasoning has enabled substantial progress in many fields, including hardware and software verification, theorem-proving, and artificial in- telligence. Different application scenarios may require different automated rea- soning techniques and sometimes their combination. In this course, we will study widely-used logical theories as well as algorithms for answering whether formu- las in those theories are satisfiable. We will consider state-of-the-art automated reasoning techniques for propositional logic, first-order logic, and various first- order theories, such as linear arithmetic over reals and integers, uninterpreted functions, bit-vectors, and arrays. We will also consider ways to reason about combinations of those theories. Topics include: logical foundations, SAT-solving, techniques for first-order theorem proving, decision procedures for different first- order theories, theory combination, the DPLL(T) framework, and applications of automated reasoning in program analysis and hardware verification. Prerequisites: CS154 Introduction to the Theory of Computation, or CS106b Programming Abstractions and CS103 Mathematical Foundations of Computing, or consent of instructor
Terms: Aut
| Units: 3
CS 259Q:
Quantum Computing
This course introduces the basics of quantum computing. Topics include: qubits, entanglement, and non-local correlations; quantum gates, circuits, and compilation algorithms; basic quantum algorithms such as Simon's algorithm and Grover's algorithm; Shor's factoring algorithm and the hidden subgroup problem; Hamiltonian simulation; stabilizer circuits, the Gottesman-Knill theorem, and the basics of quantum error correction. Prerequisites: Knowledge of linear algebra & discrete probability, and knowledge of algorithms OR quantum mechanics (or both)
Terms: Aut
| Units: 3
CS 261:
Optimization and Algorithmic Paradigms
Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty. Prerequisite: 161 or equivalent.
Terms: Aut
| Units: 3
CS 263:
Counting and Sampling
This course will cover various algorithm design techniques for two intimately connected class of problems: sampling from complex probability distributions and counting combinatorial structures. A large part of the course will cover Markov Chain Monte Carlo techniques: coupling, stationary times, canonical paths, Poincare and log-Sobolev inequalities. Other topics include correlation decay in spin systems, variational techniques, holographic algorithms, and polynomial interpolation-based counting. Prerequisites: CS161 or equivalent, STAT116 or equivalent.
Terms: Aut
| Units: 3
CS 265:
Randomized Algorithms and Probabilistic Analysis (CME 309)
Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Terms: Aut
| Units: 3
CS 269I:
Incentives in Computer Science (MS&E 206)
Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, reputation systems, social choice, and social network analysis. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. Prerequisites: CS106B/X and CS161, or permission from the instructor.
Terms: Spr
| Units: 3
CS 270:
Modeling Biomedical Systems (BIOMEDIN 210)
At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites: CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win, Spr
| Units: 3
CS 272:
Introduction to Biomedical Data Science Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)
Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr
| Units: 3-5
CS 273B:
Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Experts in the field will present guest lectures. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Spr
| Units: 3
CS 273C:
Cloud Computing for Biology and Healthcare (BIOMEDIN 222, GENE 222)
Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr
| Units: 3
CS 274:
Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology. Lectures are supplemented with assignments and programming projects, which allow students to implement important computational biology algorithms. Firm prerequisite: CS 106B. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut
| Units: 3-4
CS 275:
Translational Bioinformatics (BIOE 217, BIOMEDIN 217, GENE 217)
Analytic and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
Terms: Spr
| Units: 3-4
CS 275A:
Symbolic Musical Information (MUSIC 253)
Properties of symbolic data for music applications including advanced notation systems, data durability, mark-up languages, optical music recognition, and data-translation tasks. Hands-on work involves these digital score formats: Guido Music Notation, Humdrum, MuseData, MEI, MusicXML, SCORE, and MIDI internal code.
Terms: Win
| Units: 2-4
CS 275B:
Computational Music Analysis (MUSIC 254)
Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.
Terms: Spr
| Units: 2-4
CS 277:
Foundation Models for Healthcare (BIODS 271, RAD 271)
Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models. Prerequisites: Familiarity with machine learning principles at the level of CS 229, 231N, or 224N
Terms: Win
| Units: 3
CS 278:
Social Computing (SOC 174, SOC 274)
Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems - platforms for social media, online communities, and collaboration - to be effective and responsible? This course covers design patterns for social computing systems and the foundational ideas that underpin them.
Terms: Spr
| Units: 3-4
CS 279:
Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279)
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut
| Units: 3
CS 281:
Ethics of Artificial Intelligence
Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.
Terms: Spr
| Units: 3-4
CS 288:
Applied Causal Inference with Machine Learning and AI (MS&E 228)
Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies. Prerequisites: basic knowledge of probability and statistics. Recommended: 226 or equivalent.
Terms: Win
| Units: 3
CS 293:
Empowering Educators via Language Technology (EDUC 473)
This course explores the use of natural language processing (NLP) to support educators, by discovering, measuring, and analyzing high-leverage teaching practices. Topics include computational social science methods, ethics, bias and fairness, automated scoring, causal analyses, large language models, among others. Engaging with relevant papers, students will work towards a final project using NLP methods and a critical social scientific lens. Projects are pitched to a jury of educators at the end of the course.
Terms: Aut
| Units: 2-4
CS 295:
Software Engineering
Software specification, testing and verification. The emphasis is on automated tools for developing reliable software. The course covers material---drawn primarily from recent research papers---on the technologyunderlying these tools. Assignments supplement the lectures with hands-on experience in using these tools and customizing them for solving new problems. The course is appropriate for students intending to pursue research in program analysis and verification, as well as for those who wish to add the use of advanced software tools to their skill set. Prerequisites: 108. Recommended: a project course such as 140, 143 or 145.
Terms: Win
| Units: 3
CS 298:
Seminar on Teaching Introductory Computer Science (EDUC 298)
Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Terms: Aut
| Units: 1
CS 300:
Departmental Lecture Series
Priority given to first-year Computer Science Ph.D. students. CS Masters students admitted if space is available. Presentations by members of the department faculty, each describing informally his or her current research interests and views of computer science as a whole.
Terms: Aut
| Units: 1
CS 309A:
Cloud Computing Seminar
For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.
Terms: Aut
| Units: 1
| Repeatable
for credit
CS 323:
The AI Awakening: Implications for the Economy and Society
Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. Enrollment by application: https://digitaleconomy.stanford.edu/about/the-ai-awakening-implications-for-the-economy-and-society/
Terms: Spr
| Units: 3-4
CS 324H:
History of Natural Language Processing
Intellectual history of computational linguistics, natural language processing, and speech recognition, using primary sources. Reading of seminal early papers, interviews with early pioneers, with the goal of understanding the origins and intellectual development of the field. Prerequisites: (strictly required) completion of a Stanford graduate NLP course (CS 224C/N/U/S, 329X, 384).
Terms: Win
| Units: 3-4
CS 325B:
Data for Sustainable Development (EARTHSYS 162, EARTHSYS 262)
The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut
| Units: 3-5
| Repeatable
for credit
CS 326:
Topics in Advanced Robotic Manipulation
This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. It includes classical concepts that are still widely used and recent approaches that have changed the way we look autonomous manipulation. We cover approaches towards motion planning and control using visual and tactile perception as well as machine learning. This course is especially concerned with new approaches for overcoming challenges in generalization from experience, exploration of the environment, and learning representation so that these methods can scale to real problems. Students are expected to present one paper in a tutorial, debate a paper once from the Pro and once from the Con side. They are also expected to propose an original research project and work on it towards a research paper. Recommended: CS 131, 223A, 229 or equivalents.
Terms: Aut
| Units: 3-4
CS 328:
Foundations of Causal Machine Learning
Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics may include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and double/debiased machine learning, theoretical foundations of high-dimensional linear regression, theoretical foundations of non-linear regression models, such as random forests and neural networks, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation, adaptive experimentation and inference.
Terms: Aut
| Units: 3
| Repeatable
for credit
CS 329H:
Machine Learning from Human Preferences
Machine learning (ML) from human preferences provides mechanisms for capturing human feedback, which is used to design loss functions or rewards that are otherwise difficult to specify quantitatively, e.g., for socio-technical applications such as algorithmic fairness and many language and robotic tasks. While learning from human preferences has emerged as an increasingly important component of modern machine learning, e.g., credited with advancing the state of the art in language modeling and reinforcement learning, existing approaches are largely reinvented independently in each subfield, with limited connections drawn among them. This course will cover the foundations of learning from human preferences from first principles and outline connections to the growing literature on the topic. This includes: Inverse reinforcement learning, which uses human preferences to specify the reinforcement learning reward function; Metric elicitation, which uses human preferences to specify tradeoffs for cost-sensitive classification; Reinforcement learning from human feedback, where human preferences are used to align a pre-trained language model. This is a graduate-level course. By the end of the course, students should be able to understand and implement state-of-the-art learning from human feedback and be ready to conduct research on these topics. Prerequisites: Recommend CS 221 and CS 229
Terms: Aut
| Units: 3
CS 329M:
Machine Programming
The field of machine programming (MP) is concerned with the automation of software development. Given the recent advances in software algorithms, hardware efficiency and capacity, and an ever increasing availability of code data, it is now possible to train machines to help develop software. In this course, we teach students how to build real-world MP systems. We begin with a high-level overview of the field, including an abbreviated analysis of state-of-the-art (e.g., Merly Mentor). Next, we discuss the foundations of MP and the key areas for innovation, some of which are unique to MP. We close with a discussion of current limitations and future directions of MP. This course includes a nine-week hands-on project, where students (as individuals or in a small group) will create their own MP system and demonstrate it to the class. This course is primary intended for graduate students (it is not recommended for undergraduate students without first reviewing that the course prerequisites are met).
Terms: Aut
| Units: 3-4
CS 329R:
Race and Natural Language Processing (LINGUIST 281A, PSYCH 257A)
The goal of this practicum is to integrate methods from natural language processing with social psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing. Prerequisite: CS224N, PSYCH290, or equivalent background in natural language processing. Students interested in participating should complete the online application for permission at https://web.stanford.edu/class/cs329r/. Limited enrollment.
Terms: Aut
| Units: 3
CS 329T:
Trustworthy Machine Learning
This course will provide an introduction to state-of-the-art ML methods designed to make AI more trustworthy. The course focuses on four concepts: explanations, fairness, privacy, and robustness. We first discuss how to explain and interpret ML model outputs and inner workings. Then, we examine how bias and unfairness can arise in ML models and learn strategies to mitigate this problem. Next, we look at differential privacy and membership inference in the context of models leaking sensitive information when they are not supposed to. Finally, we look at adversarial attacks and methods for imparting robustness against adversarial manipulation.Students will gain understanding of a set of methods and tools for deploying transparent, ethically sound, and robust machine learning solutions. Students will complete labs, homework assignments, and discuss weekly readings. Prerequisites: CS229 or similar introductory Python-based ML class; knowledge of deep learning such as CS230, CS231N; familiarity with ML frameworks in Python (scikit-learn, Keras) assumed.
Terms: Aut
| Units: 3
CS 330:
Deep Multi-task and Meta Learning
While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes: goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster; meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly; curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer. This is a graduate-level course. By the end of the course, students should be able to understand and implement the state-of-the-art multi-task learning algorithms and be ready to conduct research on these topics. Prerequisites: CS 229 or equivalent. Familiarity with deep learning, reinforcement learning, and machine learning is assumed.
Terms: Aut
| Units: 3
CS 336:
Language Modeling from Scratch
Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment. Application required, apply at https://docs.google.com/forms/d/e/1FAIpQLSdW0HgT8MHzdM8cgapLWqX2ZPP1yHSX52R_r5JzF52poqXsHg/viewform
Terms: Spr
| Units: 3-5
CS 337:
AI-Assisted Care (MED 277)
AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.
Terms: Aut
| Units: 1-4
CS 339R:
Collaborative Robotics (ME 326)
This course focuses on how robots can be effective teammates with other robots and human partners. Concepts and tools will be reviewed for characterizing task objectives, robot perception and control, teammate behavioral modeling, inter-agent communication, and team consensus. We will consider the application of these tools to robot collaborators, wearable robotics, and the latest applications in the relevant literature. This will be a project-based graduate course, with the implementation of algorithms in either python or C++.
Terms: Win
| Units: 3
CS 340R:
Rusty Systems
Language shapes thought; for 40 years, software systems and some of their research challenges have been defined by the C language. In the past 5 years, this has begun to change, with new languages (Rust, Go, coq) becoming competitors to C in large classes of systems. CS340R is a project-centric course that examines how the Rust programming language changes software systems, solving some problems while presenting new ones. This course seeks to ask and start to answer a simple question: "What are the most important open research challenges for software systems written in Rust?"
Terms: Spr
| Units: 3
CS 342:
Building for Digital Health (MED 253)
This project-based course will provide a comprehensive overview of key requirements in the design and full-stack implementation of a digital health research application. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. Student teams learn about all necessary approval processes to deploy a digital health solution (data privacy clearance/I RB approval, etc.) and be guided in the development of front-end and back-end infrastructure using best practices. The final project will be the presentation and deployment of a fully approved digital health research application. CS106A, CS106B, Recommended: CS193P/A, CS142, CS47, CS110. Limited enrollment for this course. Apply for enrollment permission here: https://stanforduniversity.qualtrics.com/jfe/form/SV_9ThVhqf4zyhzheS
Terms: Win
| Units: 3-4
CS 343D:
Domain-Specific Programming Models and Compilers
This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. We will study programming models from the recent literature, categorize them, and discuss their properties. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory. Prerequisites: CS143 or equivalent
Terms: Win
| Units: 3
CS 343S:
Domain-Specific Language Design Studio
This is a design-studio course for the creation of domain-specific languages (DSLs). We will start with lectures teaching fundamental skills for designing and implementing DSLs, followed by a long term project designing and implementing a DSL of the student's choice. The course will particularly emphasize the role that languages can play in tasks that we do not usually think of as programming, such as DSLs for knitting patterns or geometric constructions.
Terms: Spr
| Units: 3
CS 347:
Human-Computer Interaction: Foundations and Frontiers
(Previously numbered CS376.) How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and comment on multiple research papers per week, and perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or CS247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.
Terms: Spr
| Units: 3-4
| Repeatable
for credit
CS 348C:
Computer Graphics: Animation and Simulation
Core mathematics and methods for computer animation and motion simulation. Traditional animation techniques. Physics-based simulation methods for modeling shape and motion: particle systems, constraints, rigid bodies, deformable models, collisions and contact, fluids, and fracture. Animating natural phenomena. Methods for animating virtual characters and crowds. Additional topics selected from data-driven animation methods, realism and perception, animation systems, motion control, real-time and interactive methods, and multi-sensory feedback. Recommended: CS 148 and/or 205A. Prerequisite: linear algebra.
Terms: Win
| Units: 3
CS 348I:
Computer Graphics in the Era of AI
This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and imaging. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts to researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, view synthesis, colorization, style transfer, motion synthesis, differentiable physics simulation, and reinforcement learning. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, apply neural procedural modeling for shape and scene synthesis, exploit data-driven methods for simulating physical phenomena, and implement policy learning algorithms for creating character animation. Recommended Prerequisites: CS148, CS231N
Terms: Win
| Units: 3-4
CS 348K:
Visual Computing Systems
Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Prerequisites: CS 107 or equivalent. Highly recommended: Parallel Computing (CS149) or Computer Architecture (EE 282). Students will benefit from some background in deep learning (CS 230, CS 231N), computer vision (CS 231A), digital image processing (CS 232) or computer graphics (CS248).
Terms: Spr
| Units: 3-4
CS 349D:
Cloud Computing Technology
The largest change in the computer industry over the past twenty years has arguably been the emergence of cloud computing: organizations are increasingly developing their workloads to be cloud native, using global-scale compute, storage, and communication services that were simply not possible with private infrastructure. This research seminar covers the latest technical advances and open issues in cloud computing, including cloud infrastructure for AI inference and training, cloud databases and data lakes, resource management, serverless computing, confidential computing, multi-cloud computing, and AI for cloud management. Students will propose and develop an original research project in cloud computing. Prerequisites: Background in computer systems recommended but not required ( CS 111/240, 144/244, 244B or 245).
Terms: Spr
| Units: 3
CS 349H:
Software Techniques for Emerging Hardware Platforms (EE 292Y)
Research seminar on software techniques for emerging computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on the semester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, exible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms. Prerequisites: CS107 or CS107E (required) and EE180 (recommended).
Terms: Aut
| Units: 3
CS 352B:
Blockchain Governance
This course offers an overview of blockchain governance and Decentralized Autonomous Organizations (DAOs), with topics including DAO tooling, on-chain and off-chain voting, delegation, constitutional design, alternative governance mechanisms, identity, and privacy. We will cover these topics and others from technical, social science, and legal perspectives, and we will include a range of guests from the web3 space as well as several speakers who are on the frontiers of DAO research. The course presumes some basic familiarity with blockchain and cryptocurrencies, but deep technical facility is not required, i.e., successful completion of CS 251 or LAW 1043 is more than enough. Elements used in grading: Homework and papers. There are no examinations. Grading elements and the course itself are designed so that students with diverse expertise and backgrounds (law, technical, business, etc.) have an equal opportunity to do well and have a powerful learning experience. Cross-listed with LAW 1078. The course will be taught in law school classrooms. In addition to the listed Stanford faculty instructors and the various guest speakers, Silke Noa Elrifai, a crypto lawyer and mathematician with a deep background in actual DAO projects and currently a Visiting Scholar at Stanford, will be the primary instructor for several classes and will play an integral role in the course.
Terms: Spr
| Units: 3
CS 353:
Seminar on Logic & Formal Philosophy (PHIL 391)
Contemporary work. May be repeated a total of three times for credit.
Terms: Aut, Win
| Units: 2-4
| Repeatable
3 times
(up to 12 units total)
CS 355:
Advanced Topics in Cryptography
Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. May be repeated for credit. Prerequisite: CS255.
Terms: Spr
| Units: 3
| Repeatable
for credit
CS 356:
Topics in Computer and Network Security
Research seminar covering foundational work and current topics in computer and network security. Students will read and discuss published research papers as well as complete an original research project in small groups. Open to Ph.D. and masters students as well as advanced undergraduate students. Prerequisites: While the course has no official prerequisites, students need a mature understanding of software systems and networks to be successful. We strongly encourage students to first take CS155: Computer and Network Security.
Terms: Aut
| Units: 3
CS 360:
Simplicity and Complexity in Economic Theory (ECON 284)
Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.
Terms: Spr
| Units: 3-5
CS 361:
Engineering Design Optimization (AA 222)
Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.
Terms: Spr
| Units: 3-4
CS 366:
Computational Social Choice (MS&E 336)
An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Win
| Units: 3
CS 369O:
Optimization Algorithms (CME 334, MS&E 312)
Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure. Prerequisite: linear algebra, multivariable calculus, probability, and proofs.
Terms: Win
| Units: 3
CS 372:
Artificial Intelligence for Precision Medicine and Psychiatric Disorders
Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.
Terms: Spr
| Units: 3
CS 375:
Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249)
The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such "convergent man-machine evolution", including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS229, CS230, CS231n, CS234, CS236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).
Terms: Win
| Units: 3
CS 377G:
Designing Serious Games
Over the last few years we have seen the rise of "serious games" to promote understanding of complex social and ecological challenges, and to create passion for solving them. This project-based course provides an introduction to game design principals while applying them to games that teach. Run as a hands-on studio class, students will design and prototype games for social change and civic engagement. We will learn the fundamentals of games design via lecture and extensive reading in order to make effective games to explore issues facing society today. The course culminates in an end-of- quarter open house to showcase our games. Prerequisite: CS147 or equivalent. 247G recommended, but not required.
Terms: Aut
| Units: 3-4
CS 377Q:
Designing for Accessibility (ME 214)
Designing for accessibility is a valuable and important skill in the UX community. As businesses are becomeing more aware of the needs and scope of people with some form of disability, the benefits of universal design, where designing for accessibility ends up benefiting everyone, are becoming more apparent. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility through individual assignments. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability. This class focuses on the accessibility of UX with computers, mobile phones, VR, and has a design class prerequisite (e.g., CS147, ME115A).
Terms: Spr
| Units: 3-4
CS 377U:
Understanding Users
This project-based class focuses on understanding the use of technology in the world. Students will learn generative and evaluative research methods to explore how systems are appropriated into everyday life in a quarter-long project where they design, implement and evaluate a novel mobile application. Quantitative (e.g. A/B testing, instrumentation, analytics, surveys) and qualitative (e.g. diary studies, contextual inquiry, ethnography) methods and their combination will be covered along with practical experience applying these methods in their project. Prerequisites: CS 147, 193A/193P (or equivalent mobile programming experience).
Terms: Spr
| Units: 3-4
CS 390A:
Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Anari, N. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Chang, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fischer, M. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kennedy, M. (PI);
Khatib, O. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Rubinstein, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schramm, T. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Sosic, R. (PI);
Stanford, J. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Troccoli, N. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 390B:
Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Anari, N. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Chang, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubinstein, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schramm, T. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Troccoli, N. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 390C:
Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS 390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
A. Hudson, D. (PI);
Achour, S. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Lam, M. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Troccoli, N. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wu, J. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Rutherford, E. (GP)
CS 390D:
Part-time Curricular Practical Training
For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science PhD students engage in research and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. Students on F1 visas should be aware that completing 12 or more months of full-time CPT will make them ineligible for Optional Practical Training (OPT).
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hayden, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubinstein, A. (PI);
Saberi, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yan, L. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 399:
Independent Project
Letter grade only. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. Letter grade; if not appropriate, enroll in CS399P. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Adeli, E. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Akbarpour, M. (PI);
Altman, R. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Chang, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Demszky, D. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hennessy, J. (PI);
Ho, D. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Ma, T. (PI);
MacCartney, B. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Patrignani, M. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Rubin, D. (PI);
Rubinstein, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Socher, R. (PI);
Sosic, R. (PI);
Stanford, J. (PI);
Subramonyam, H. (PI);
Syrgkanis, V. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Varodayan, D. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wodtke, C. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yan, L. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 399P:
Independent Project
Graded satisfactory/no credit. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. S/NC only; if not appropriate, enroll in CS399. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Fox, E. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Lee, C. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Liu, K. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Shoham, Y. (PI);
Socher, R. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Varodayan, D. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Wodtke, C. (PI);
Wu, J. (PI);
Yan, L. (PI);
Yang, D. (PI);
Young, P. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 407:
Lytics Seminar (EDUC 407)
(Same as GSBGID 307) Students will learn to design technology mediated learning environments for adult learners, conduct research in those environments, and learn from prior EdTech failures. Grounded in various theoretical frameworks that inform the design of learning environments, the course explores how people learn and the evidence of learning that can be collected and modeled in online environments in real world contexts. The course also examines specific case studies of failed EdTech ventures to identify patterns and causes of failure. Throughout the course we will consider ethical issues related to design and research in human learning. Overall, this course will provide students with a foundation in learning theory and the skills and knowledge needed to design, implement, and evaluate effective technology mediated learning environments.
Terms: Spr
| Units: 1-4
| Repeatable
4 times
(up to 16 units total)
CS 422:
Interactive and Embodied Learning (EDUC 234A)
Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction. Prerequisites: CS229, CS231N, CS234 (or equivalent).
Terms: Win
| Units: 3
| Repeatable
5 times
(up to 15 units total)
CS 432:
Computer Vision for Education and Social Science Research (EDUC 463)
Computer vision -- the study of how to design artificial systems that can perform high-level tasks related to image or video data (e.g. recognizing and locating objects in images and behaviors in videos) -- has seen recent dramatic success. In this course, we seek to give education and social science researchers the know-how needed to apply cutting edge computer vision algorithms in their work as well as an opportunity to workshop applications. Prerequisite: python familiarity and some experience with data.
Terms: Win
| Units: 3
CS 448B:
Data Visualization (EDUC 458, SYMSYS 195V)
Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. There are no official prerequisites for the class, but familiarity with the material in CS147, CS148 and CS142 is especially useful. Most important is a basic working knowledge of, or willingness to learn, web- programming, especially JavaScript, Vega-Lite and D3.js.
Terms: Aut
| Units: 3-4
| Repeatable
for credit
CS 448I:
Computational Imaging (EE 367)
Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.
Terms: Win
| Units: 3
CS 470:
Music and AI (MUSIC 356)
How do we make music with artificial intelligence? What does it mean to do so (and is it even a good idea)? How might we design systems that balance machine automation and human interaction? More broadly, how do we want to live with our technologies? Are there - and ought there be - limits to using AI for art? (And what is Art, anyway?) In this "critical making" course, students will learn practical tools and techniques for AI-mediated music creation, engineer software systems incorporating AI, HCI and Music, and critically reflect on the aesthetic and ethical dimensions of technology.
Terms: Win
| Units: 3-4
CS 476A:
Music, Computing, Design: The Art of Design (MUSIC 256A)
This course explores the artful design of software tools, toys, games,ninstruments, and experiences. Topics include programming, audiovisualndesign, strategies for crafting interactive systems, game design, asnwell as aesthetic and social considerations of shaping technology in ournworld today. Course work features several programming assignments withnan emphasis on critical design feedback, reading responses, and an"design your own" final project. Prerequisite: experience in C/C++/Javanor Unity/C#. See https://ccrma.stanford.edu/courses/256a/
Terms: Aut
| Units: 3-4
CS 498C:
Introduction to CSCL: Computer-Supported Collaborative Learning (EDUC 315A)
This seminar introduces students to foundational concepts and research on computer-supported collaborative learning (CSCL). It is designed for LSTD doctoral students, LDT masters' students, other GSE graduate students and advanced undergraduates inquiring about theory, research and design of CSCL. CSCL is defined as a triadic structure of collaboration mediated by a computational artefact (participant-artifact-participant). CSCL encompasses two individuals performing a task together in a short time, small or class-sized groups, and students following the same course, digitally interacting.
Terms: Win
| Units: 3
CS 498D:
Design for Learning: Generative AI for Collaborative Learning (DESIGN 292, EDUC 449)
Would you like to design ways to use generative AI to help humans learn with other humans? In this course, you will develop creative ways to use generative AI to support collaborative learning, also learning more about AI as researchers continue to improve tools like ChatGPT. In creating new learning activities that could be used at Stanford or in other courses, you will build experience with fundamentals of design, including the design abilities of learning from others, navigating ambiguity, synthesizing information, and experimenting rapidly. You will do this by tackling real design challenges presented by our project partners, which include several Stanford programs, while drawing on your own first-hand experience as students. This class is open to all students, undergraduate and graduate, of any discipline. No previous design experience or experience with AI is required. Just a collaborative spirit and hard work.
Terms: Aut
| Units: 3
CS 499:
Advanced Reading and Research
Letter grade only. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Letter grade; if not appropriate, enroll in CS499P.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Adeli, E. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Akbarpour, M. (PI);
Altman, R. (PI);
Anari, N. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Boahen, K. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Fox, J. (PI);
Ganguli, S. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hayden, P. (PI);
Hennessy, J. (PI);
Ho, D. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kennedy, M. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Linderman, S. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McClelland, J. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Raina, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubin, D. (PI);
Rubinstein, A. (PI);
Saberi, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Utterback, C. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yan, L. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 499P:
Advanced Reading and Research
Graded satisfactory/no credit. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. S/NC only; if not appropriate, enroll in CS499.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Adeli, E. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Akbarpour, M. (PI);
Altman, R. (PI);
Anari, N. (PI);
Bailis, P. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boahen, K. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Fox, J. (PI);
Ganguli, S. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hayden, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kennedy, M. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Linderman, S. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McClelland, J. (PI);
McKeown, N. (PI);
Mirhoseini, A. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Raina, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubin, D. (PI);
Rubinstein, A. (PI);
Saberi, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Utterback, C. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yan, L. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
CS 522:
Seminar in Artificial Intelligence in Healthcare
Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at https://tinyurl.com/cs522-stanford
Terms: Aut
| Units: 1
CS 528:
Machine Learning Systems Seminar
Machine learning is driving exciting changes and progress in computing systems. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? What challenges does industry face when deploying machine learning systems in the real world, and how can new system designs meet those challenges? In this weekly talk series, we will invite speakers working at the frontier of machine learning systems, and focus on how machine learning changes the modern programming stack. Topics will include programming models for ML, infrastructure to support ML applications such as ML Platforms, debugging, parallel computing, and hardware for ML. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
3 times
(up to 3 units total)
CS 529:
Robotics and Autonomous Systems Seminar (AA 289)
Seminar talks by researchers and industry professionals on topics related to modern robotics and autonomous systems. Broadly, talks will cover robotic design, perception and navigation, planning and control, and learning for complex robotic systems. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
(up to 99 units total)
CS 547:
Human-Computer Interaction Seminar
Weekly speakers on human-computer interaction topics. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
CS 802:
TGR Dissertation
Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Agrawala, M. (PI);
Aiken, A. (PI);
Akbarpour, M. (PI);
Altman, R. (PI);
Anari, N. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boahen, K. (PI);
Bohg, J. (PI);
Boneh, D. (PI);
Bouland, A. (PI);
Boyd, S. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
Dill, D. (PI);
Dror, R. (PI);
Durumeric, Z. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fatahalian, K. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Finn, C. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Fox, E. (PI);
Fox, J. (PI);
Ganguli, S. (PI);
Genesereth, M. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Guestrin, C. (PI);
Guibas, L. (PI);
Haber, N. (PI);
Hanrahan, P. (PI);
Hashimoto, T. (PI);
Hayden, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Icard, T. (PI);
James, D. (PI);
Johari, R. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kennedy, M. (PI);
Khatib, O. (PI);
Kjoelstad, F. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koyejo, S. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Landay, J. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Linderman, S. (PI);
Liu, K. (PI);
Ma, T. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McClelland, J. (PI);
McKeown, N. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Okamura, A. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (PI);
Pavone, M. (PI);
Pea, R. (PI);
Piech, C. (PI);
Plotkin, S. (PI);
Plummer, R. (PI);
Potts, C. (PI);
Prabhakar, B. (PI);
Pratt, V. (PI);
Raghavan, P. (PI);
Raina, P. (PI);
Rajaraman, A. (PI);
Re, C. (PI);
Reingold, O. (PI);
Roberts, E. (PI);
Rosenblum, M. (PI);
Rubin, D. (PI);
Rubinstein, A. (PI);
Sadigh, D. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Tan, L. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Ullman, J. (PI);
Utterback, C. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Wu, J. (PI);
Yamins, D. (PI);
Yang, D. (PI);
Yeung, S. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
Rutherford, E. (GP)
