Results for CS

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.

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!

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/

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

Additional design and implementation problems to complement the material taught in CS111. In-class participation is required. Prerequisite: consent of instructor. Corequisite: CS111

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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

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

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.

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.

Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS194. Preference given to seniors. Prerequisites: CS109 and CS161.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

(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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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/

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

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?"

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.

(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.

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).

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).

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.

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.

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.

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.

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.

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).

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).

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.

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.

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.

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).

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.

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.

(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.

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.

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.

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.

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.

Weekly speakers on human-computer interaction topics. May be repeated for credit.

Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.