CS 1C:
Introduction to Computing at Stanford
For those with limited experience with computers or who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Final project. Not a programming course.
Terms: Aut
| Units: 1
CS 1U:
Practical Unix
A practical introduction to using the Unix operating system with a focus on Linux command line skills. Class will consist of video tutorials and weekly hands-on lab sections. The time listed on AXESS is for the first week's logistical meeting only. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Topics may be added, given sufficient interest. Course website: http://cs1u.stanford.edu
Terms: Aut, Win, Spr
| Units: 1
CS 9:
Problem-Solving for the CS Technical Interview
This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Drawing on multiple sources of actual interview questions, students will learn key problem-solving strategies specific to the technical/coding interview. Students will be encouraged to synthesize information they have learned across different courses in the major. Emphasis will be on the oral and combination written-oral modes of communication common in coding interviews, but which are unfamiliar settings for problem solving for many students. Prerequisites: CS 106B or X.
Terms: Aut
| Units: 1
CS 20SI:
Tensorflow for Deep Learning Research
This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments. Prerequisites: CS229 or CS224D/N.
Terms: Win
| Units: 2
CS 22A:
The Social & Economic Impact of Artificial Intelligence
Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon 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 systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems 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 respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Terms: Spr
| Units: 1
CS 27:
Literature and Social Online Learning (COMPLIT 239B, ENGLISH 239B)
Study, develop, and test new digital methods, games, apps, interactive social media uses to innovate how the humanities can engage and educate students and the public today. Exploring well-known literary texts, digital storytelling forms and literary communities online, students work individually and in interdisciplinary teams to develop innovative projects aimed at bringing literature to life. Tasks include literary role-plays on Twitter; researching existing digital pedagogy and literary projects, games, and apps; reading and coding challenges; collaborative social events mediated by new technology. Minimal prerequisites which vary for students in CS and the humanities; please check with instructors.
Last offered: Autumn 2014
| Units: 3-5
| UG Reqs: WAY-A-II
CS 41:
Hap.py Code: The Python Programming Language
The fundamentals and contemporary usage of the Python programming language. Primary focus on developing best practices in writing Python and exploring the extensible and unique parts of Python that make it such a powerful language. Topics include: data structures (e.g. lists and dictionaries) and characteristic pythonic conventions such as anonymous functions, iterables, and powerful built-ins (e.g. map, filter, zip). We will also cover object-oriented design, the standard library, and common third-party packages (e.g. requests, pillow). Time permitting, we will explore modern Python-based web frameworks and project distribution. Prerequisite: 106B/X or equivalent. Application required.
Terms: Spr
| Units: 2
CS 42:
Callback Me Maybe: Contemporary Javascript
Introduction to the JavaScript programming language with a focus on building contemporary applications. Course consists of in-class activities and programming assignments that challenge students to create functional web apps (e.g. Yelp, Piazza, Instagram). Topics include syntax/semantics, event-based programming, document object model (DOM), application programming interfaces (APIs), asynchronous JavaScript and XML (AJAX), jQuery, Node.js, and MongoDB. Prerequisite: CS 107.
Terms: Aut
| Units: 2
CS 45N:
Computers and Photography: From Capture to Sharing
Preference to freshmen with experience in photography and use of computers. Elements of photography, such as lighting, focus, depth of field, aperture, and composition. How a photographer makes photos available for computer viewing, reliably stores them, organizes them, tags them, searches them, and distributes them online. No programming experience required. Digital SLRs and editing software will be provided to those students who do not wish to use their own.
Terms: Aut
| Units: 3-4
| UG Reqs: WAY-CE
CS 50:
Using Tech for Good
Students in the class will work in small teams to implement high-impact projects for partner organizations. Taught by the CS+Social Good team, the aim of the class is to empower you to leverage technology for social good by inspiring action, facilitating collaboration, and forging pathways towards global change. Recommended: CS 106B, CS 42 or 142. Class is open to students of all years.May be repeat for credit
Terms: Aut, Spr
| Units: 2
| Repeatable
5 times
(up to 10 units total)
CS 51:
CS + Social Good Studio: Building Social Impact Projects for Change
Get real-world experience launching and developing your own social impact projects! Students will 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 nonprofit domain experts. The class aims to provide an outlet, along with the resources, for students to create social change through CS, while providing students with experience engaging in the full product development cycle on real-world projects. Prerequisite: CS 147, equivalent experience, or consent of instructors.
Terms: Win
| Units: 2
CS 52:
CS + Social Good: Implementing Sustainable Social Impact Projects
Continuation of CS51 (Building Social Impact Projects for Change). 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 54N:
Great Ideas in Computer Science
Stanford Introductory Seminar. Preference to freshmen. Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. No prior experience with programming is assumed. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; and the philosophy behind artificial intelligence.
Terms: Aut
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
CS 74N:
Digital Dilemmas
Preference to freshmen. Issues where policy decision making requires understanding computer and communications technology. Technology basics taught in non-technology terms. Topics include consumer privacy, government surveillance, file sharing and intellectual property, and electronic voting.
Last offered: Autumn 2013
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
CS 82:
Social Impacts of Media Innovation
Media innovation merges technical and cultural development and benefits diverse social groups in different ways. Considering historic media innovations such as cinema, hip-hop, and the works of innovator in residence Paul D. Miller aka DJ Spooky, the course focuses on what ideas benefit whom. Lectures and workshops underscore the need to innovate to survive and get heard, and offer know-how for radical innovation in the arts and entertainment industry. Course projects will be considered for inclusion in the Stanford Humanities Showcase. Open to both undergraduate and graduate students.
Terms: Spr
| Units: 1
CS 95SI:
Functional Programming in Clojure
Clojure is a dialect of Lisp that runs on the JVM, CLR, or Javascript engine. This course explores the fundamentals and philosophy of Clojure, with emphasis on the benefits of immutability and functional programming that make it such a powerful and fun language. Topics include: immutability, functional programming (function composition, higher order functions), concurrency (atoms, promises, futures, actors, Software Transactional Memory, etc), LISP (REPL-driven development, homoiconicity, macros), and interop (between Clojure code and code native to the host VM). The course also explores design paradigms and looks at the differences between functional programing and object-oriented programing, as well as bottom-up versus top-down design.
Terms: Aut
| Units: 2
CS 96SI:
Mobilizing Healthcare - iOS Development for Mobile Health
How can mobile technology can be leveraged to tackle pressing problems in healthcare? Our class will feature guest lecturers from Verily (formerly Google Life Sciences), Apple Health, and mobile health companies in developing countries and in the Bay Area. This class will give an overview of the fundamentals and contemporary usage of iOS development with a Mobile Health focus. Primary focus on developing best practices for Apple HealthKit and ResearchKit among other tools for iOS application development. Students will complete a project in the mobile health space sponsored and advised by professionals and student TAs. Recommended: CS193P or iOS development at a similar level. Apply at https://enrollcs96si.typeform.com/to/FGGHVl by Sept 30.
Terms: Aut
| Units: 2
CS 101:
Introduction to Computing Principles
Introduces the essential ideas of computing: data representation, algorithms, programming "code", computer hardware, networking, security, and social issues. Students learn how computers work and what they can do through hands-on exercises. In particular, students will see the capabilities and weaknesses of computer systems so they are not mysterious or intimidating. Course features many small programming exercises, although no prior programming experience is assumed or required. CS101 is not a complete programming course such as CS106A. CS101 is effectively an alternative to CS105. A laptop computer is recommended for the in-class exercises.
Terms: Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci
CS 102:
Big Data: Tools and Techniques, Discoveries and Pitfalls
Aimed primarily at students who may not major in CS but want to learn about big data and apply that knowledge in their areas of study. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing massive data sets, but it is surprisingly easy to come to false conclusions from data analysis alone, and privacy of data connected to individuals can be a major concern. This course provides a broad introduction to big data: historical context and case studies; privacy issues; data analysis techniques including databases, data mining, and machine learning; sampling and statistical significance; data analysis tools including spreadsheets, SQL, Python, R; data visualization techniques and tools. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: high school AP computer science, CS106A, or other equivalent programming experience; comfort with statistics and spreadsheets helpful but not required.
Terms: Spr
| Units: 3-4
| UG Reqs: WAY-AQR
CS 103:
Mathematical Foundations of Computing
Mathematical foundations required for computer science, including propositional predicate logic, induction, sets, functions, and relations. Formal language theory, including regular expressions, grammars, finite automata, Turing machines, and NP-completeness. Mathematical rigor, proof techniques, and applications. Prerequisite: 106A or equivalent.
Terms: Aut, Spr
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
Instructors: ;
Schwarz, K. (PI);
Ahmed, D. (TA);
Desai, S. (TA);
Frank-Fischer, L. (TA);
Gibbons, K. (TA);
Huang, J. (TA);
Jeje, K. (TA);
Maduabum, M. (TA);
McDowell, B. (TA);
Meister, M. (TA);
Naik, J. (TA);
Palamuttam, R. (TA);
Rottman-Yang, S. (TA);
Saplitski, A. (TA);
Tang, R. (TA);
Wang, K. (TA)
CS 103A:
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, Spr
| Units: 1
CS 105:
Introduction to Computers
For non-technical majors. What computers are and how they work. Practical experience in programming. Construction of computer programs and basic design techniques. 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
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106A:
Programming Methodology (ENGR 70A)
Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. No prior programming experience required. Summer quarter enrollment is limited.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106B:
Programming Abstractions (ENGR 70B)
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. Summer quarter enrollment is limited.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
CS 106J:
Programming Methodology in JavaScript
Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style. This course covers the same material as CS 106A but does so using JavaScript, the most common language for implementing interactive web pages, instead of Java. No prior programming experience required. Enrollment limited to 100.
Terms: Win, Spr
| Units: 3-5
CS 106L:
Standard C++ Programming Laboratory
Supplemental lab to 106B and 106X. Additional features of standard C++ programming practice. Possible topics include advanced C++ language features, standard libraries, STL containers and algorithms, object memory management, operator overloading, and inheritance. Prerequisite: consent of instructor. Corequisite: 106B or 106X.
Terms: Aut, Spr
| Units: 1
CS 106S:
Programming Abstractions and Social Good
Supplemental lab to CS 106B and CS 106X. Students will apply fundamental computer science concepts learned in 106B/X to problems in the social good space (such as health, government, education, and environment). Course consists of in-class activities designed by local tech companies and nonprofits. Corequisite: 106B or 106X.
Terms: Win
| Units: 1
CS 106X:
Programming Abstractions (Accelerated) (ENGR 70X)
Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Additional advanced material and more challenging projects. Prerequisite: excellence in 106A or equivalent, or consent of instructor.
Terms: Aut, Win
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
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
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: ;
Lee, C. (PI);
Clow, J. (TA);
Corcoran, B. (TA);
Geis, T. (TA);
Louie, J. (TA);
Palocz, S. (TA);
Smith, S. (TA);
Smythe, S. (TA);
Tebuev, D. (TA);
Valdes, S. (TA);
Volk, M. (TA);
Wang, D. (TA);
Warshaw, D. (TA);
Zhao, A. (TA)
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. Prerequisite: 106B or X, and consent of instructor.
Terms: 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: Aut, 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: Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
CS 109L:
Statistical Computing with R Laboratory
Supplemental lab to CS109. Introduces the R programming language for statistical computing. Topics include basic facilities of R including mathematical, graphical, and probability functions, building simulations, introductory data fitting and machine learning. Provides exposure to the functional programming paradigm. Corequisite: CS109.
Last offered: Spring 2015
| Units: 1
CS 110:
Principles of Computer Systems
Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; security, and encryption; and performance optimizations. Prerequisite: 107.
Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci
CS 124:
From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, genome sequences, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, genomic sequence alignment, spell checking, speech processing, recommender systems. Prerequisite: CS103, CS107, CS109.
Terms: Win
| Units: 3-4
CS 131:
Computer Vision: Foundations and Applications
Robots that can navigate space and perform duties, search engines that can index billions of images and videos, algorithms that can diagnose medical images for diseases, or smart cars that 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. Course will introduce a number of fundamental concepts in computer vision and expose students to a number of real-world applications, plus guide students through a series of well designed projects such that they will get to implement cutting-edge computer vision algorithms. Prerequisites: Students should be familiar with Matlab (i.e. have programmed in Matlab before) and Linux; plus Calculus & Linear Algebra.
Terms: Aut
| Units: 3-4
CS 140:
Operating Systems and Systems Programming
Operating systems design and implementation. Basic structure; synchronization and communication mechanisms; implementation of processes, process management, scheduling, and protection; memory organization and management, including virtual memory; I/O device management, secondary storage, and file systems. Prerequisite: CS 110.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 142:
Web Applications
Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment. Prerequisites: CS 107 and CS 108.
Terms: Win, Spr
| Units: 3
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, and 107.
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, packet switching, layered architectures. Applications: web/http, voice-over-IP, p2p file sharing and socket programming. Reliable transport: TCP/IP, reliable transfer, flow control, and congestion control. The network layer: names and addresses, routing. Local area networks: ethernet and switches. Wireless networks and network security. Prerequisite: CS 110.
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 145:
Introduction to Databases
The course covers database design and the use of database management systems for applications. It includes extensive coverage of the relational model, relational algebra, and SQL.The course includes database design and relational design principles based on dependencies and normal forms. Many additional key database topics from the design and application-building perspective are also covered: indexes, views, transactions, authorization, integrity constraints, triggers, on-line analytical processing (OLAP), JSON, and emerging NoSQL systems. Class time will include guest speakers from industry and additional advanced topics as time and class interest permits. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors: ;
Re, C. (PI);
Balakrishnan, T. (GP);
Fang, H. (TA);
Ginzberg, A. (TA);
Johnson, K. (TA);
Kostas, L. (TA);
Mai, D. (TA);
Peddada, A. (TA);
Pyke, J. (TA);
Stewart, R. (TA);
Viswanathan, S. (TA);
Zeng, C. (TA);
Zhao, L. (TA)
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.
Terms: Aut
| Units: 3-5
Instructors: ;
Landay, J. (PI);
Gregory, K. (TA);
Kim, W. (TA);
Leung, S. (TA);
Ngu, A. (TA);
Park, E. (TA);
Raghvendra, S. (TA);
Tang, E. (TA);
Yang-Sammataro, J. (TA);
Zhai, K. (TA)
CS 148:
Introduction to Computer Graphics and Imaging
Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Focuses on using OpenGL to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Course will cover fundamental understanding of light and color, as well as how it impacts computer displays and printers. Class will discuss more thoroughly how light interacts with the environment, constructing engineering models such as the BRDF, plus various simplifications into more basic lighting and shading models. Also covers ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided 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 toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Prerequisites: CS 107, MATH 51.
Terms: Aut, Sum
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-CE
Instructors: ;
Fedkiw, R. (PI);
Hyde, D. (PI);
Bao, M. (TA);
Hyde, D. (TA);
Kao, S. (TA);
Lin, P. (TA);
Loh, W. (TA);
Lu, W. (TA);
Riggs, J. (TA);
Winter, H. (TA);
Yu, Y. (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, futures, 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 110 and have taken CS 143.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 154:
Introduction to Automata and Complexity Theory
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
CS 155:
Computer and Network Security
For 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 code. Prerequisite: 110. Recommended: basic Unix.
Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
CS 157:
Logic and Automated Reasoning
An elementary exposition from a computational point of view of propositional and predicate logic, axiomatic theories, and theories with equality and induction. Interpretations, models, validity, proof, strategies, and applications. Automated deduction: polarity, skolemization, unification, resolution, equality. Prerequisite: 103 or 103B.
Terms: Aut
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
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: 103 or 103B; 109 or STATS 116.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: ;
Charikar, M. (PI);
Eng, D. (PI);
Valiant, G. (PI);
Wootters, M. (PI);
Chen, W. (TA);
Choi, S. (TA);
Geri, O. (TA);
Hancock, B. (TA);
Hildick-Smith, S. (TA);
Jiang, H. (TA);
Johnston, L. (TA);
Khandwala, N. (TA);
Kim, A. (TA);
Kim, S. (TA);
Li, S. (TA);
Nigam, P. (TA);
Oshri, B. (TA);
Wu, J. (TA);
Yang, W. (TA)
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.
Terms: Spr
| Units: 3-4
| UG Reqs: WAY-CE
| Repeatable
4 times
(up to 16 units total)
CS 173:
A Computational Tour of the Human Genome
(Only one of 173 or 273A counts toward any CS degree program.) Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing; functional landscape of the human genome (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures on current genomic research topics. Class will be similar in spirit to CS273A, which will not be offered this year. Prerequisites: CS107 or equivalent background in programming.
Last offered: Winter 2013
| Units: 3
CS 181:
Computers, Ethics, and Public Policy
Primarily for majors entering computer-related fields. 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: 106B or X.
Terms: Win, 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.
Terms: Win, Spr
| Units: 4
| UG Reqs: GER:EC-EthicReas, WAY-ER
CS 183E:
Effective Leadership in High-Tech
You will undoubtedly leave Stanford with the technical skills to excel in your first few jobs. But non-technical skills are just as critical to making a difference. This seminar is taught by two industry veterans in engineering leadership and product management. In a small group setting, we will explore how you can be a great individual contributor (communicating with clarity, getting traction for your ideas, resolving conflict, and delivering your best work) and how you can transition into leadership roles (finding leadership opportunities, creating a great team culture, hiring and onboarding new team members). We will end by turning back to your career (picking your first job and negotiating your offer, managing your career changes, building a great network, and succeeding with mentors). Prerequisites: Preference given to seniors and co-terms in Computer Science and related majors.
Terms: Aut
| Units: 1
CS 183F:
Startup School: The First 100 Days
Starting a company is hard. Starting a company and building it into a successful, lasting business is even more so. This course aims to teach the fundamentals of starting a startup through a 10-week interactive class, with the goal of decreasing the barrier to entry for new entrepreneurs. Instruction will focus primarily on the first 100 days, from ideation to execution, covering the minutiae of company structure, product design, core metric evaluation, ethics and so on. Lectures will be taught by experts in the startup space, accompanied by small group learning sessions with active entrepreneurs.
Terms: Spr
| Units: 2
CS 191:
Senior Project
Restricted to Computer Science and Computer Systems Engineering students. Group or individual 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 substantial programming and modern user-interface technologies 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. Required public presentation of final application or research results. Prerequisite: Completion of at least 135 units.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Angst, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Nilsson, N. (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);
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);
Sosic, R. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 191W:
Writing Intensive Senior Project (WIM)
Restricted to Computer Science and Computer Systems Engineering students. Writing-intensive version of CS191. Register using the section number of an Academic Council member. Prerequisite: Completion of at least 135 units.
Terms: Aut, Win, Spr
| Units: 3-6
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (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);
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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (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.
Terms: Aut, Win, Spr, Sum
| Units: 1-4
| Repeatable
for credit
Instructors: ;
Aiken, A. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (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);
Trevisan, L. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 193A:
Android Programming
Introduction to building applications for Android platform. Examines key concepts of Android programming: tool chain, application life-cycle, views, controls, intents, designing mobile UIs, networking, threading, and more. Features weekly lectures and a series of small programming projects. Phone not required, but a phone makes the projects more engaging. Prerequisites: 106B or Java experience at 106B level. Enrollment limited and application required: https://goo.gl/forms/ACKDYowYfOZD4vNd2
Terms: Win
| Units: 3
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 193P:
iPhone and iPad Application Programming
Tools and APIs required to build applications for the iPhone and iPad platforms using the iOS SDK. User interface design for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Swift programming language. Other topics include: object-oriented database API, animation, mobile device power management, multi-threading, networking and performance considerations. Prerequisites: C language and object-oriented programming experience exceeding 106B or X level. Previous completion of any one of the following is required: CS 107, 108 (preferred) or 110. Recommended: UNIX, graphics, databases.
Terms: Win
| Units: 3
CS 193S:
Scalability Engineering
Learn to solve real world engineering challenges in this programming project course. Scale projects not just from the coding and engineering perspective, but use those same techniques to increase usability, popularity, development velocity and maintainability. Discover how engineering applies to project ideation, self and team development, customer acquisition, user experience. As we build applications, we will cover tools and practices for scalable programming including: the javascript ecosystem, containers and cloud platforms, agile development, growth hacking. We focus on rapid feedback loops to build better systems faster. In one quarter, develop scalable habits to build apps designed to grow. Application required. Prerequisites: one or more of CS 140, 142, 145, 147.
Terms: Win
| Units: 3
CS 193X:
Web Programming Fundamentals
Introduction to full-stack web development with an emphasis on fundamentals. Client-side topics include layout and rendering through HTML and CSS, event-driven programming through JavaScript, and single-threaded asynchronous programming techniques including Promises. Focus on modern standardized APIs and best practices. Server-side topics include the development of RESTful APIs, JSON services, and basic server-side storage techniques. Covers desktop and mobile web development. Enrollment is limited. Application required. Apply at https://goo.gl/forms/nlQte0OqVpU6maRq2 byn11:59pm Tuesday, March 28. nPrerequisite: 106B or equivalent.
Terms: Spr
| Units: 3-5
CS 194:
Software Project
Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes a detailed proposal. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeat for credit. Prerequisites: CS 110 and CS 161.
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.
Terms: Win, Spr
| Units: 3
CS 196:
Computer Consulting
Focus is on Macintosh and Windows operating system maintenance and troubleshooting through hardware and software foundation and concepts. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. Not a programming course. Prerequisite: 1C or equivalent.
Terms: Win, Spr
| Units: 2
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. Letter grade; if not appropriate, enroll in 199P.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (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);
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);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 199P:
Independent Work
(Staff)
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Altman, R. (PI);
Angst, R. (PI);
Baker, M. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (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);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 202:
Law for Computer Science Professionals
Intellectual property law as it relates to computer science including copyright registration, patents, and trade secrets; contract issues such as non-disclosure/non-compete agreements, license agreements, and works-made-for-hire; dispute resolution; and principles of business formation and ownership. Emphasis is on topics of current interest such as open source and the free software movement, peer-to-peer sharing, encryption, data mining, and spam.
Terms: Aut, Spr
| Units: 1
CS 203:
Cybersecurity: A Legal and Technical Perspective
This class will use the case method to teach basic computer, network, and information security from a technology, law, policy, and business perspective. Using recent security incidents from the news, we will discuss the technical aspects of the incident, the legal and policy aspects of the problem, and business approaches to managing breaches. The case studies will be organized around the following topics: tracking political dissidents, state sponsored sabotage, corporate and government espionage, credit card theft, theft of embarrassing personal data, phishing and social engineering attacks, denial of service attacks, attacks on weak session management and URLs, cloud data storage as a security risk, wiretapping on the Internet, and digital forensics.nStudents taking the class will learn about the techniques attackers use, applicable legal prohibitions, rights, and remedies, and approaches to managing the risk and aftermath of an attack. Grades will be based on class participation (25%) and on a student term paper explaining the technical and legal concepts relevant to a recent cybersecurity breach of the student's choice, with instructor approval (75%). The class will be co-taught by Stanford Professor of Computer Science and Electrical Engineering and co-director of the Stanford Computer Security Lab Dan Boneh and Director of Civil Liberties at the Law School's Center for Internet and Society Jennifer Granick. Special Instructions: This class is limited to 80 students, with an effort made to have students from SLS (40 students will be selected by lottery) and students from Computer Science (40 students). Elements used in grading: Class Participation, Final Paper. Cross-listed with Computer Science (Same as Law 4004).
Terms: Spr
| Units: 2
CS 204:
Legal Informatics
Legal informatics based on representation of regulations in computable form. Encoding regulations facilitate creation of legal information systems with significant practical value. Convergence of technological trends, growth of the Internet, advent of semantic web technology, and progress in computational logic make computational law prospects better. Topics: current state of computational law, prospects and problems, philosophical and legal implications. This course is *Cross* listed with LAW 4019. Prerequisite: basic concepts of programming.
Terms: Spr
| Units: 2-3
CS 205A:
Mathematical Methods for Robotics, Vision, and Graphics
Continuous mathematics background necessary for research in robotics, vision, and graphics. Possible topics: linear algebra; the conjugate gradient method; ordinary and partial differential equations; vector and tensor calculus. Prerequisites: 106B or X; MATH 51; or equivalents.
Terms: Spr
| Units: 3
CS 205B:
Mathematical Methods for Fluids, Solids, and Interfaces
Numerical methods for simulation of problems involving solid mechanics and fluid dynamics. Focus is on practical tools needed for simulation, and continuous mathematics involving nonlinear hyperbolic partial differential equations. Possible topics: finite element method, highly deformable elastic bodies, plasticity, fracture, level set method, Burgers' equation, compressible and incompressible Navier-Stokes equations, smoke, water, fire, and solid-fluid coupling. Prerequisite: 205A or equivalent.
Last offered: Spring 2009
| Units: 3
CS 206:
Exploring Computational Journalism (COMM 281)
This course will explore the evolving field of computational journalism. Students will research and discuss the state of field in five areas where computation is affecting journalism: AI, Data Science, and Info Viz; Emerging Hardware Tech, including Drones, Sensors, and VR; Audience Participation and Diverse Viewpoints; Free Speech and Democracy; and News Ecosystems and Business Models. Admission by application; please email James Hamilton at jayth@stanford.edu to request an application.
Terms: Win
| Units: 3
CS 208E:
Great Ideas in Computer Science
Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material. Enrollment limited to students in the Master's program in Computer Science Education.
Terms: Aut
| Units: 3
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: CS 109 and 110.
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 213:
Creating Great VR: From Ideation to Monetization
Covering everything from VR fundamentals to futurecasting to launch management, this course will expose you to best practices and guidance from VR leaders that helps positions you to build great VR experiences.
Terms: Spr
| Units: 1
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, Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience).
Terms: Aut
| Units: 3-4
Instructors: ;
Liang, P. (PI);
Abdulhamid, A. (TA);
Agrawal, A. (TA);
Anenberg, B. (TA);
Bhatnagar, P. (TA);
Cai, Y. (TA);
Caswell, I. (TA);
Chen, M. (TA);
Dasu, G. (TA);
Dong, C. (TA);
Goel, K. (TA);
Han, A. (TA);
Hsu, I. (TA);
Khandwala, N. (TA);
Kim, H. (TA);
LaRow, W. (TA);
Lee, T. (TA);
Moore, D. (TA);
Musa, R. (TA);
Rai, K. (TA);
Shi, T. (TA);
Sohmshetty, A. (TA);
Wang, L. (TA);
Wang, Y. (TA);
Wu, K. (TA);
Yan, K. (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 224D:
Deep Learning for Natural Language Processing
Deep learning approaches have obtained very high performance across many different natural language processing tasks. In this class, students will learn to understand, implement, train, debug, visualize and potentially invent their own neural network models for a variety of language understanding tasks. The course provides a deep excursion from early models to cutting-edge research. Applications will range across a broad spectrum: from simple tasks like part of speech tagging, over sentiment analysis to question answering and machine translation. The final project will involve implementing a complex neural network model and applying it to a large scale NLP problem. We will introduce a common programming framework for deep learning for the problem sets.Prerequisites: programming abilities (python), linear algebra, Math 21 or equivalent, machine learning background (CS 229 or similar) Recommended: CS 224N, EE364a (convex optimization), CS 231N
Last offered: Spring 2016
| Units: 3-4
CS 224N:
Natural Language Processing with Deep Learning (LINGUIST 284)
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 or CS121/221.
Terms: Win
| Units: 3-4
| Repeatable
for credit
CS 224S:
Spoken Language Processing (LINGUIST 285)
Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Automatic speech recognition, speech synthesis, dialogue management, and applications to digital assistants, search, and spoken language understanding systems. Covers state-of-the-art approaches based on deep learning as well as traditional methods. Prerequisites: CS 124, 221, 224N, or 229
Terms: Spr
| Units: 2-4
CS 224U:
Natural Language Understanding (LINGUIST 188)
Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221; and logical/semantics such as LINGUIST 130A or B, CS 157, or PHIL150
Last offered: Spring 2016
| Units: 3-4
CS 224W:
Social and Information Network Analysis
How do diseases spread? Who are the influencers? How can we predict friends and enemies in a social network? How information flows and mutates as it is passed through networks? Behind each of these questions there is an intricate wiring diagram, a network, that defines the interactions between the components. And we will never understand these questions unless we understand the networks behind them. The course will cover recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics include methods for link analysis and network community detection, diffusion and information propagation on the web, virus outbreak detection in networks, and connections with work in the social sciences and economics.
Terms: Aut
| Units: 3-4
Instructors: ;
Leskovec, J. (PI);
Ahmed, T. (TA);
Benson, A. (TA);
Gallegos, Y. (GP);
Hamilton, W. (TA);
Liu, Z. (TA);
Siroker, M. (GP);
Su, J. (TA);
Ulmer, B. (TA);
Yao, L. (TA)
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 225B:
Robot Programming Laboratory
For robotics and non-robotics students. Students program mobile robots to exhibit increasingly complex behavior (simple dead reckoning and reactivity, goal-directed motion, localization, complex tasks). Topics: motor control and sensor characteristics; sensor fusion, model construction, and robust estimation; control regimes (subsumption, potential fields); probabalistic methods, including Markov localization and particle filters. Student programmed robot contest. Programming is in C++ on Unix machines, done in teams. Prerequisite: programming at the level of 106B, 106X, 205, or equivalent.
Last offered: Autumn 2011
| 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 228T:
Probabilistic Graphical Models: Advanced Methods
For students interested in advanced methods in machine learning and probabilistic AI. Describes the theoretical foundations for methods of inference and learning in probabilistic graphical models, allowing for the derivation of properties of these methods and for the development of more advanced methods. Sample topics include advanced methods in Markov chain Monte Carlo, approximate message-passing algorithms for inference derived from an optimization perspective, representation and inference in models involving continuous variables, learning undirected models, learning with hidden variables, and non-parametric Bayesian methods. Prerequisites: CS228; strong mathematical foundation.
Last offered: Spring 2012
| Units: 3
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, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Terms: Aut
| Units: 3-4
Instructors: ;
Duchi, J. (PI);
Ng, A. (PI);
Agrawal, P. (TA);
Bhargava, R. (TA);
Desai, N. (TA);
Dixit, K. (TA);
Germain, F. (TA);
Ji, J. (TA);
Katanforoosh, K. (TA);
Kumar, P. (TA);
Levy, D. (TA);
Meng, C. (TA);
Pai, S. (TA);
Ruban, T. (TA);
Seshadri, A. (TA);
Sheng, H. (TA);
Tian, Y. (TA);
Wang, B. (TA);
Wang, D. (TA);
Xie, Z. (TA);
Yin, Z. (TA);
Zhou, B. (TA);
Zhu, M. (TA)
CS 229T:
Statistical Learning Theory (STATS 231)
How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include classical asymptotics, method of moments, generalization bounds via uniform convergence, kernel methods, online learning, and multi-armed bandits. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (STATS 315A or CS 229). Convex optimization (EE 364A) is helpful but not required.
Terms: Spr
| Units: 3
CS 231A:
Computer Vision: From 3D Reconstruction to Recognition
(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:
Convolutional Neural Networks for Visual Recognition
Computer Vision has become ubiquitous in our society, with applications innsearch, image understanding, apps, mapping, medicine, drones, andnself-driving cars. Core to many of these applications are the tasks of image classification, localization and detection. This course is a deep dive into details of neural network architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week 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. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge: http://image-net.org/challenges/LSVRC/2014/index. Prerequisites: Proficiency in Python; familiarity with C/C++; CS 131 and CS 229 or equivalents; Math 21 or equivalent, linear algebra.
Terms: Spr
| Units: 3-4
CS 232:
Digital Image Processing (EE 368)
Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended: EE261, EE278.
Terms: Aut
| Units: 3
CS 233:
Geometric and Topological Data Analysis (CME 251)
Mathematical 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. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for geometric data analysis. Non-linear dimensionality reduction. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps.Networks of data sets and joint analysis for segmentation and labeling. The emergence of abstractions or concepts from data. Prerequisites: discrete algorithms at the level of 161; linear algebra at the level of CME103.
| 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 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
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.
Last offered: Winter 2016
| Units: 3-4
CS 240:
Advanced Topics in Operating Systems
Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.
Terms: Spr
| Units: 3
| Repeatable
for credit
CS 241:
Embedded Systems Workshop (EE 285)
Project-centric building hardware and software for embedded computing systems. Students work on an existing project of their own or join one of these projects. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, deterministic timing and synchronization, state-based programming models, filters, frequency response, and high-frequency signals, low power operation, system and PCB design, security, and networked communication. Prerequisite: CS107 (or equivalent).
Terms: Aut
| Units: 3
| Repeatable
3 times
(up to 9 units total)
CS 242:
Programming Languages
Central concepts in modern programming languages, impact on software development, language design trade-offs, and implementation considerations. Functional, imperative, and object-oriented paradigms. Formal semantic methods and program analysis. Modern type systems, higher order functions and closures, exceptions and continuations. Modularity, object-oriented languages, and concurrency. Runtime support for language features, interoperability, and security issues. Prerequisite: 107, or experience with Lisp, C, and an object-oriented language.
Terms: Aut
| Units: 3
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 244C:
Readings and Projects in Distributed Systems
Companion project option for 244B. Corequisite: 244B.
Last offered: Spring 2010
| Units: 3-6
CS 245:
Database Systems Principles
File organization and access, buffer management, performance analysis, and storage management. Database system architecture, query optimization, transaction management, recovery, concurrency control. Reliability, protection, and integrity. Design and management issues. Prerequisites: 145, 161.
Terms: Win
| Units: 3
CS 246:
Mining Massive Data Sets
The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Topics include: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommender Systems, Clustering, Link Analysis, Large-scale machine learning, Data streams, Analysis of Social-network Graphs, and Web Advertising. Prerequisites: At lease one of CS107 or CS145; At least one of CS109 or STAT116, or equivalent.
Terms: Win
| Units: 3-4
CS 246H:
Mining Massive Data Sets Hadoop Lab
Supplement to CS 246 providing additional material on Hadoop. Students will learn how to implement data mining algorithms using Hadoop, how to implement and debug complex MapReduce jobs in Hadoop, and how to use some of the tools in the Hadoop ecosystem for data mining and machine learning. Topics: Hadoop, MapReduce, HDFS, combiners, secondary sort, distributed cache, SQL on Hadoop, Hive, Cloudera ML/Oryx, Mahout, Hadoop streaming, implementing Hadoop jobs, debugging Hadoop jobs, TF-IDF, Pig, Sqoop, Oozie, HBase, Impala. Prerequisite: CS 107 or equivalent.
Terms: Win
| Units: 1
CS 247:
Human-Computer Interaction Design Studio
Project-based focus on interaction design process, especially early-stage design and rapid prototyping. Methods used in interaction design including needs analysis, user observation, sketching, concept generation, scenario building, and evaluation. Prerequisites: 147 or equivalent background in design thinking; 106B or equivalent background in programming. Recommended: CS 142 or equivalent background in web programming. Enrollment limited to 40 students based on an application to be given out the first day of class.
Terms: Win, Spr
| Units: 3-4
CS 247L:
Human Computer Interaction Technology Laboratory
Hands-on introduction to contemporary HCI technologies. Interaction design with Adobe Flash, mobile development, physical computing, and web applications. Corequisite: 247.
Last offered: Spring 2014
| Units: 1
CS 248:
Interactive Computer Graphics
This is the second course in the computer graphics sequence, and as such it assumes a strong familiarity with rendering and image creation. The course has a strong focus on computational geometry, animation, and simulation. Topics include splines, implicit surfaces, geometric modeling, collision detection, animation curves, particle systems and crowds, character animation, articulation, skinning, motion capture and editing, rigid and deformable bodies, and fluid simulation. As a final project, students implement an interactive video game utilizing various concepts covered in the class. Games may be designed on mobile devices, in a client/server/browser environment, or on a standard personal computer. Prerequisite: CS148.
Terms: Win
| Units: 3-4
CS 250:
Error Correcting Codes: Theory and Applications (EE 387)
Introduction to the theory of error correcting codes, emphasizing diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; constructions like Reed-Solomon, Reed-Muller, and expander codes; list-decoding, list-recovery and locality. Applications include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and com-pressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say, CS109, CME106 or EE178), and ¿mathematical maturity¿ (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Aut
| Units: 3
CS 251:
Bitcoin and Crypto Currencies
For advanced undergraduates and for graduate students. The potential applications for Bitcoin-like technologies is enormous. The course will cover the technical aspects of crypto-currencies, blockchain technologies, and distributed consensus. Students will learn how these systems work and how to engineer secure software that interacts with the Bitcoin network and other crypto currencies. 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; diagonalization; space complexity: PSPACE, Savitch's theorem, and NL=coNL; counting problems and #P-completeness; circuit complexity; pseudorandomness and derandomization; complexity of approximation; quantum computing; complexity barriers. Prerequisites: 154 or equivalent; mathematical maturity.
Terms: Aut
| 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 257:
Logic and Artificial Intelligence (PHIL 356C)
This is a course at the intersection of philosophical logic and artificial intelligence. The goal of the course is to understand the role that expressive logical frameworks might play in AI, and to gain a deeper understanding of how different logical systems relate, and what features of a logical system could make it useful for representation and/or reasoning. Specific themes may include: 1. Tradeoff between complexity and expressivity 2. Capturing subtle reasoning about agent mental states 3. Defeasibility, causality, and the relation between logic and probability 4. Logical formalizations of legal and normative reasoning 5. Combining statistical learning and inference with rich logical structure 6. Logical systems close to the structure of natural language ("natural logics"). Prerequisites: It is expected that students already have a solid background in logic. Phil 151 is ideal, but Phil 150 or CS 157 would be acceptable, with the understanding that there may be some catching up to do. 2 unit option for PhD students only.
Terms: Win
| Units: 2-4
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 online algorithms. Prerequisite: 161 or equivalent.
Terms: Win
| Units: 3
CS 263:
Algorithms for Modern Data Models (MS&E 317)
We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of CS 261.
Last offered: Spring 2015
| Units: 3
CS 264:
Beyond Worst-Case Analysis
This course is motivated by problems for which the traditional worst-case analysis of algorithms fails to differentiate meaningfully between different solutions, or recommends an intuitively "wrong" solution over the "right" one. This course studies systematically alternatives to traditional worst-case analysis that nevertheless enable rigorous and robust guarantees on the performance of an algorithm. Topics include: instance optimality; smoothed analysis; parameterized analysis and condition numbers; models of data (pseudorandomness, locality, diffuse adversaries, etc.); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Prerequisites: CS161 (required). CS261 is recommended but not required.
Terms: Win
| 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 267:
Graph Algorithms
An introduction to advanced topics in graph algorithms. Focusing on a variety of graph problems, the course will explore topics such as small space graph data structures, approximation algorithms, dynamic algorithms, and algorithms for special graph classes. Topics include: approximation algorithms for shortest paths and graph matching, distance oracles, graph spanners, cliques and graph patterns, dynamic algorithms, graph coloring, algorithms for planar graphs. Prerequisites: 161 or the equivalent mathematical maturity.
Terms: Aut
| Units: 3
CS 268:
Geometric Algorithms
Techniques for design and analysis of efficient geometric algorithms for objects in 2-, 3-, and higher dimensions. Topics: convexity, triangulations and simplicial complexes, sweeping, partitioning, and point location. Voronoi/Delaunay diagrams and their properties. Arrangements of curves and surfaces. Intersection and visibility problems. Geometric searching and optimization. Random sampling methods. Range searching. Impact of numerical issues in geometric computation. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. Prerequisite: discrete algorithms at the level of 161. Recommended: 164.
Terms: Aut
| Units: 3
CS 269G:
Almost Linear Time Graph Algorithms (MS&E 313)
Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.
Terms: Win
| Units: 3
CS 269I:
Incentives in Computer Science
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: Aut
| Units: 3
CS 269O:
Introduction to Optimization Theory (MS&E 213)
Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.
Terms: Spr
| Units: 3
CS 270:
Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)
Methods for modeling biomedical systems and for building model-based software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire hands-on experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology.
Terms: Win
| Units: 3
CS 272:
Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)
Capstone Biomedical Informatics (BMI) 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 211 or 214 or 217. Preference to BMI graduate students. Consent of instructor required.
Terms: Spr
| Units: 3-5
CS 273A:
A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A)
Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing (technologies, assembly, personalized sequencing); functional landscape (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures from genomic researchers. No prerequisites. Seehttp://cs273a.stanford.edu/.
Terms: Aut
| Units: 3
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. 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 CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.
Terms: Aut
| Units: 3
CS 274:
Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units.
Terms: Aut
| Units: 3-4
CS 275:
Translational Bioinformatics (BIOE 217, BIOMEDIN 217)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 4
CS 275A:
Symbolic Musical Information (MUSIC 253)
Focus on symbolic data for music applications including advanced notation systems, optical music recognition, musical data conversion, and internal structure of MIDI files.
Terms: Win
| Units: 2-4
CS 275B:
Music Query, Analysis, and Style Simulation (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 276:
Information Retrieval and Web Search (LINGUIST 286)
Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites: CS 107, CS 109, CS 161.
Terms: Spr
| Units: 3
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 294A:
Research Project in Artificial Intelligence
Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.
Last offered: Winter 2012
| Units: 3
| Repeatable
for credit
CS 294H:
Research Project in Human-Computer Interaction
Student teams under faculty supervision work on research and implementationnof a large project in HCI. State-of-the-art methods related to the problemndomain. Prerequisites CS 377, 147, 247, or permission from instructor.
Last offered: Winter 2010
| Units: 3
CS 294S:
Research Project in Software Systems and Security
Topics vary. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. May be repeated for credit. Prerequisites: CS 103 and 107.
Terms: Spr
| Units: 3
| Repeatable
for credit
CS 294W:
Writing Intensive Research Project in Computer Science
Restricted to Computer Science and Computer Systems Engineering undergraduates. Students enroll in the CS 294W section attached to the CS 294 project they have chosen.
Terms: Spr
| Units: 3
CS 295:
Software Engineering
Software specification, testing, and verification. Emphasis is on modern technology for developing reliable software at a reasonable cost. Assignments focus on applying these techniques to realistic software systems. 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: Spr
| 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 309:
Industrial Lectureships in Computer Science
Guest computer scientist. By arrangement. May be repeated for credit.
| Units: 1
| Repeatable
for credit
CS 309A:
Cloud Computing
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 315A:
Parallel Computer Architecture and Programming
The principles and tradeoffs in the design of parallel architectures. Emphasis is on naming, latency, bandwidth, and synchronization in parallel machines. Case studies on shared memory, message passing, data flow, and data parallel machines illustrate techniques. Architectural studies and lectures on techniques for programming parallel computers. Programming assignments on one or more commercial multiprocessors. Prerequisites: EE 282, and reasonable programming experience.
Last offered: Spring 2011
| Units: 3
CS 315B:
Parallel Computing Research Project
Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project. Prerequisite: consent of instructor.
Terms: Aut
| Units: 3
CS 316:
Advanced Multi-Core Systems
In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites: EE 180 (formerly 108B). Recommended: CS 149, EE 282.
Terms: Aut
| Units: 3
CS 319:
Topics in Digital Systems
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Units: 3
| Repeatable
for credit
CS 323:
Automated Reasoning: Theory and Applications
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.
Terms: Spr
| Units: 3-4
CS 327A:
Advanced Robotic Manipulation
Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.
Terms: Spr
| Units: 3
CS 328:
Topics in Computer Vision
Fundamental issues of, and mathematical models for, computer vision. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. May be repeated for credit. Prerequisites: 205, 223B, or equivalents.
| Units: 3
| Repeatable
for credit
CS 329:
Topics in Artificial Intelligence
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Units: 3
| Repeatable
for credit
CS 329M:
Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning
This advanced graduate course explores in depth several important classes of algorithms in modern machine learning. We will focus on understanding the mathematical properties of these algorithms in order to gain deeper insights on when and why they perform well. We will also study applications of each algorithm on interesting, real-world settings. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Students will learn mathematical techniques for analyzing these algorithms and hands-on experience in using them. We will supplement the lectures with latest papers and there will be a significant research project component to the class. Prerequisites: Probability (CS 109), linear algebra (Math 113), machine learning (CS 229), and some coding experience.
Terms: Spr
| Units: 3
CS 331B:
Representation Learning in Computer Vision
This course surveys recent developments in representation learning that are relevant to visual recognition and understanding tasks. In particular we will examine: 1) why representations matter; 2) classical and moderns methods of forming and learning representations in 2D and 3D computer vision; 3) how to close the loop between sensing and action for perception robotics; 4) how to connect visual-based representations with language; 5) methods for analyzing and visualizing representations. In addition to regular lectures and talks by invited speakers, we will read advanced papers on this topic, and carry out in-depth discussions of these papers as well as the students' own research projects.
Terms: Aut
| Units: 3
CS 334A:
Convex Optimization I (CME 364A, EE 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.
Terms: Spr, Sum
| Units: 3
Instructors: ;
Angeris, G. (PI);
Boyd, S. (PI);
Momeni, A. (PI);
Agrawal, A. (TA);
Fu, R. (TA);
Hemmati, S. (TA);
Lim, R. (TA);
Mohamed, A. (TA);
Momeni, A. (TA);
Sharafat, A. (TA);
Sheng, H. (TA);
Sun, Q. (TA);
Yu, G. (TA);
Zaidi, M. (TA);
Zhang, J. (TA)
CS 340:
Topics in Computer Systems
Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.
| Units: 3-4
CS 341:
Project in Mining Massive Data Sets
Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution. Teams consist of three students each, and they will meet regularly with a "coach" chosen from participating staff. Early lectures will cover the use of Amazon EC2 and certain systems like Hadoop and Hive. Occasional lectures thereafter will feature outside speakers, special topics of interest, and progress reports by the teams. Prerequisite: CS 246.
Terms: Spr
| Units: 3
CS 344:
Topics in Computer Networks
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Last offered: Spring 2011
| Units: 3
CS 345S:
Data-intensive Systems for the Next 1000x
The last decade saw enormous shifts in the design of large-scale data-intensive systems due to the rise of Internet services, cloud computing, and Big Data processing. Where will we see the next 1000x increases in scale and data volume, and how should data-intensive systems accordingly evolve? This course will critically examine a range of trends, including the Internet of Things, drones, smart cities, and emerging hardware capabilities, through the lens of software systems research and design. Students will perform a comparative analysis by reading and discussing cutting-edge research while performing their own original research. Prerequisites: Strong background in software systems, especially databases (CS 245) and distributed systems (CS 244B), and/or machine learning (CS 229). Undergraduates who have completed CS 245 are strongly encouraged to attend.
Terms: Aut
| Units: 3-4
CS 348A:
Computer Graphics: Geometric Modeling & Processing
The mathematical tools needed for the geometrical aspects of computer graphics and especially for modeling smooth shapes. Fundamentals: homogeneous coordinates, transformations, and perspective. Theory of parametric and implicit curve and surface models: polar forms, Bézier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multi-resolution representations of geometry. Representations of solids and conversions among them. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification and parameterization. Prerequisite: linear algebra at the level of CME103. Recommended: 248.
Terms: Win
| Units: 3-4
CS 348B:
Computer Graphics: Image Synthesis Techniques
Intermediate level, emphasizing high-quality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multi-core parallelism for rendering. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.
Terms: Spr
| Units: 3-4
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: Aut
| Units: 3
CS 349:
Topics in Programming Systems
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Last offered: Winter 2006
| Units: 3
| Repeatable
for credit
CS 352:
Pseudo-Randomness
Pseudorandomness is the widely applicable theory of efficiently generating objects that look random, despite being constructed using little or no randomness. Since psudorandom objects can replace uniformly distributed ones (in a well-defined sense), one may view pseudorandomness as an extension of our understanding of randomness through the computational lens. We will study the basic tools pseudorandomness, such as limited independence, randomness extractors, expander graphs, and pseudorandom generators. We will also discuss the applications of pseudrandomness to derandomization, cryptography and more. We will cover classic result as well as cutting-edge techniques. Prerequisites: CS 154 and CS 161, or equivalents.
Terms: Spr
| Units: 3-4
CS 358:
Topics in Programming Language Theory
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Units: 3
| Repeatable
for credit
CS 359:
Topics in the Theory of Computation
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Last offered: Spring 2005
| Units: 3
| Repeatable
for credit
CS 359C:
Topics in Theory of Computation: Classics of Cryptography
This course will review some of the greatest discoveries in modern cryptography: zero-knowledge proofs, factoring algorithms, elliptic-curve cryptography, post-quantum cryptography, and more. Some of the topics we will cover have immediate practical applications. Other topics we will study for their potential future applications. And yet others we will study for the theoretical insights they provide. The course readings will be a combination of the original "classic" papers and more modern treatments of the same topics. Prerequisite: CS 255, an equivalent course, or permission of instructors.
Terms: Spr
| Units: 3
CS 361:
Introduction to Multidisciplinary Design Optimization (AA 222)
Design of engineering systems within a formal optimization framework. Engineering often involves the synthesis of several disciplines, such as fluids, structures, and controls in aerospace systems. These disciplines interact in complex ways, making the optimization of the system design challenging. 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, 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 369:
Topics in Analysis of Algorithms
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Last offered: Spring 2013
| Units: 3
| Repeatable
for credit
CS 369H:
Hierarchies of Integer Programming Relaxations
Mathematical programming relaxations of integer programming formulations are a popular way to apply convex optimization techniques to hard combinatorial optimization problems. Such relaxations can be made closer to their integer programming counterparts by adding constraints; a systematic way to achieve this is via hierarchies of relaxations. Several such hierarchies are well-studied in the literature: Lovasz-Schrijver, Sherali-Adams and the Parrilo-Lasserre sum-of-squares (SoS) hierarchy. Recently, these hierarchies have received a lot of attention due to their potential to make progress on long standing algorithmic questions, and connections to various other areas such as computational complexity, combinatorial and polynomial optimization, quantum computing, proof complexity and so on. In this course we will cover recent research results in this area for problems arising from optimization, machine learning, computational complexity and more, discussing both lower and upper bounds. Prerequisites: Mathematical maturity (required), exposure to algorithms (strongly recommended), and optimization (recommended).
Terms: Spr
| Units: 3
CS 371:
Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite: CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Win
| Units: 3
CS 373:
Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, GENE 245, STATS 345)
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Spr
| Units: 3
CS 376:
Human-Computer Interaction Research
Prepares students to conduct original HCI research by reading and discussing seminal and cutting-edge research papers. Main topics are ubiquitous computing, social computing, and design and creation; breadth topics include HCI methods, programming, visualization, and user modeling. Student pairs perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, an A- or better in CS 147 or CS 247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.
Terms: Aut
| Units: 3-4
| Repeatable
for credit
CS 377:
Topics in Human-Computer Interaction
Contents change each quarter. May be repeated for credit. See http://hci.stanford.edu/academics for offerings.
| Units: 2-3
| Repeatable
for credit
CS 377C:
Topics in HCI: Crowdsourcing and Social Computing
This project-based class focuses on the design of social computing and crowdsourcing systems. Students will learn how to engage large groups of people online, from microtask crowdsourcing to the design of online communities. The course will cover best practices for system design such as motivating participation, ethical guidelines, agreement measures, and gold standards. Advanced topics such as expert and team-based crowdsourcing, incentive design, and complex crowd workflows will also be discussed. Students will learn about the application of crowdsourcing to other areas of computer science, and how the field relates to social psychology and organizational behavior. Prerequisite: CS 147.
Terms: Spr
| Units: 3-4
CS 377D:
Topics in Learning and Technology: d.compress - Designing Calm (EDUC 328A)
Contents of the course change each year. The course can be repeated. Stress silently but steadily damages physical and emotional well-being, relationships, productivity, and our ability to learn and remember. This highly experiential and project-oriented class will focus on designing interactive technologies to enable calm states of cognition, emotion, and physiology for better human health, learning, creativity and productivity.
Last offered: Spring 2015
| Units: 3
| Repeatable
6 times
(up to 18 units total)
CS 377E:
Designing Solutions to Global Grand Challenges
In this course we creatively apply information technologies to collectively attack Global Grand Challenges (e.g., global warming, rising healthcare costs and declining access, and ensuring quality education for all). Interdisciplinary student teams will carry out need-finding within a target domain, followed by brainstorming to propose a quarter long project. Teams will spend the rest of the quarter applying user-centered design methods to rapidly iterate through design, prototyping, and testing of their solutions. This course will interweave a weekly lecture with a weekly studio session where students apply the techniques hands-on in a small-scale, supportive environment.
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 379:
Interdisciplinary Topics
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Units: 3
| Repeatable
for credit
CS 379C:
Computational Models of the Neocortex
This course emphasizes approaches to scaling the technologies of computer science and systems neuroscience to take advantage of the exponential trend in computational power known as Moore's Law. Modern methods in signal processing and machine learning are combined with technologies for managing large datasets common in industry. Classes feature scientists presenting novel approaches for analyzing the structure and function of complex neural circuits. Grading is based on class participation (30%), a project proposal due at midterm (20%), and a final project demonstration and report due by the end of finals (50%). Team projects are encouraged, especially multi-disciplinary collaborations. Prerequisites are basic high-school biology, good math skills and familiarity with machine learning. Some background in computer vision and signal processing is important for projects in structural analysis. Familiarity with modern artificial neural network technologies is a plus for projects in functional analysis. For more detail, see http://www.stanford.edu/class/cs379c/ with special attention to the CALENDAR and DISCUSSION tabs from past classes available by following the ARCHIVES link.
Terms: Spr
| Units: 3
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 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. 390 A, B, and C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fischer, M. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Nilsson, N. (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);
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);
Sosic, R. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (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 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. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pea, R. (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);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (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 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. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors: ;
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (PI);
Pea, R. (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);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390D:
Part-time 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 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 in F1 visas should be aware that completing 12 or more months of full-time CPT will make them ineligible for Optional Practical Training (OPT). 390A, B, C, D may each be taken once.
Terms: Aut
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Golub, G. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (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);
Roughgarden, T. (PI);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390P:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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
| Units: 1
Instructors: ;
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (PI);
Kozyrakis, C. (PI);
Kundaje, A. (PI);
Lam, M. (PI);
Latombe, J. (PI);
Leskovec, J. (PI);
Levis, P. (PI);
Levitt, M. (PI);
Levoy, M. (PI);
Li, F. (PI);
Liang, P. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Plotkin, S. (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);
Schwarz, K. (PI);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390Q:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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: Win, Spr
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (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);
Roughgarden, T. (PI);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390R:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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: Sum
| Units: 1
CS 390S:
Part-Time CPT
For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Plotkin, S. (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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390T:
Part-Time CPT
For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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: Win
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Plotkin, S. (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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390U:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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: Spr
| Units: 1
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Pande, V. (PI);
Parlante, N. (PI);
Plotkin, S. (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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 390V:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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
| Units: 1
CS 390W:
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 196. May be taken just once; not repeatable. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in research and integrate that work into their academic program. Students register 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
| Units: 1
CS 393:
Computer Laboratory
For CS graduate students. A substantial computer program is designed and implemented; written report required. Recommended as a preparation for dissertation research. 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: ;
Aiken, A. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (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);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 395:
Independent Database Project
For graduate students in Computer Science. Use of database management or file systems for a substantial application or implementation of components of database management system. Written analysis and evaluation required. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Aiken, A. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (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);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 399:
Independent Project
Letter grade only.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Borenstein, J. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goel, S. (PI);
Goodman, N. (PI);
Gregg, C. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
MacCartney, B. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Niebles Duque, J. (PI);
Nilsson, N. (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);
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);
Sosic, R. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 399P:
Independent Project
Graded satisfactory/no credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (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);
Shoham, Y. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 402:
Beyond Bits and Atoms: Designing Technological Tools (EDUC 236)
Practicum in designing and building technology-enabled curricula and hands-on learning environments. Students use software toolkits and state-of-the-art fabrication machines to design educational software, educational toolkits, and tangible user interfaces. The course will focus on designing low-cost technologies, particularly for urban school in the US and abroad. We will explore theoretical and design frameworks from the constructionist learning perspective, critical pedagogy, interaction design for children.
Terms: Win
| Units: 3-4
CS 402L:
Beyond Bits and Atoms - Lab (EDUC 211)
This course is a hands-on lab in the prototyping and fabrication of tangible technologies, with a special focus in learning and education. We will learn how to use state-of-the-art fabrication machines (3D printers, 3D scanners, laser cutters, routers) to design educational toolkits, educational toys, science kits, and tangible user interfaces. A special focus of the course will be to design low-cost technologies, particularly for urban school in the US and abroad.
Terms: Win
| Units: 1-3
CS 424M:
Learning Analytics and Computational Modeling in Social Science (EDUC 390)
Computational modeling and data-mining are dramatically changing the physical sciences, and more recently also the social and behavioral sciences. Traditional analysis techniques are insufficient to investigate complex dynamic social phenomena as social networks, online gaming, diffusion of innovation, opinion dynamics, classroom behavior, and other complex adaptive systems. In this course, we will learn about how modeling, network theory, and basic data-mining can support research in cognitive, and social sciences, in particular around issues of learning, cognitive development, and educational policy.
Last offered: Winter 2013
| Units: 3-4
CS 424P:
Extracting Social Meaning and Sentiment (LINGUIST 287)
Methods for extracting social meaning (speaker perspectives, emotions and attitudes) from text and speech. Topics include sentiment analysis and summarization, detection of deception, sarcasm, emotion, and personality.nnAnalysis of meaning-bearing characteristics of the speaker and topic, including text, discourse, prosodic and other cues. Prerequisite: CS 124 or 221 or 229 or permission of instructors.
Last offered: Autumn 2010
| Units: 3
CS 428:
Computation and cognition: the probabilistic approach (PSYCH 204)
This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Terms: Aut
| Units: 3
CS 431:
High-level Vision: From Neurons to Deep Neural Networks (PSYCH 250)
Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, Computer Science and Applied Statistics. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field. Topics include: theories of object perception, neural computations underlying invariant object perception, how visual exemplars and categories are represented in the brain, what information is present in distributed activations across neural populations and how it relates to object perception, what modern statistical and analytical tools there are for multi-variate analysis of brain activations.
Terms: Win, Spr
| Units: 3
CS 448:
Topics in Computer Graphics
Topic changes each quarter. Recent topics: computational photography, datannvisualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offererings and prerequisites. May be repeated for credit.
Last offered: Autumn 2007
| Units: 3-4
| Repeatable
for credit
CS 448B:
Data Visualization
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. Prerequisite: one of 147, 148, or equivalent.
Terms: Aut
| Units: 3
| Repeatable
for credit
CS 448H:
Topics in Computer Graphics: Agile Hardware Design
Topic changes each quarter. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offerings and prerequisites. May be repeated for credit.
Terms: Win
| Units: 3
CS 448I:
Computational Imaging and Display (EE 367)
Spawned by rapid advances in optical fabrication and digital processing power, a new generation of imaging technology is emerging: computational cameras at the convergence of applied mathematics, optics, and high-performance computing. Similar trends are observed for modern displays pushing the boundaries of resolution, contrast, 3D capabilities, and immersive experiences through the co-design of optics, electronics, and computation. This course serves as an introduction to the emerging field of computational imaging and displays. Students will learn to master bits and photons.
Terms: Win
| Units: 3
CS 468:
Topics in Geometric Algorithms: Machine Learning for 3D Data
Contents of this course change with each offering. Past offerings have included geometric matching, surface reconstruction, collision detection, computational topology. May be repeated for credit. Winter 2013/14 iteration will cover Computational Symmetry & Regularity and spring quarter 2013/14 will cover data-driven shape analysis. Prerequisites: Math 52 or equivalent, basic coding.
Terms: Spr
| Units: 3
| Repeatable
for credit
CS 476A:
Music, Computing, Design I: Art of Design for Computer Music (MUSIC 256A)
Creative design for computer music software. Programming, audiovisual design, as well as software design for musical tools, instruments, toys, and games. Provides paradigms and strategies for designing and building music software, with emphases on interactive systems, aesthetics, and artful product design. Course work includes several programming assignments and a "design+implement" final project. Prerequisite: experience in C/C++ and/or Java.See https://ccrma.stanford.edu/courses/256a/
Terms: Aut
| Units: 3-4
CS 476B:
Music, Computing, Design II: Virtual and Augmented Reality for Music (MUSIC 256B)
Aesthetics, design, and exploration of creative musical applications of virtual reality (VR) and augmented reality (AR), centered around VR and mobile technologies. Comparison between AR, VR, and traditional software design paradigms for music. Topics include embodiment, interaction design, novel instruments, social experience, software design + prototyping. Prerequisite: MUSIC 256A / CS 476A.
Last offered: Winter 2016
| Units: 3-4
CS 499:
Advanced Reading and Research
Letter grade only. Advanced reading and research for CS graduate students. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Sidford, A. (PI);
Stepp, M. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Yamins, D. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
CS 499P:
Advanced Reading and Research
Graded satisfactory/no credit. Advanced reading and research for CS graduate students. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Agrawala, M. (PI);
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Bailis, P. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Barrett, C. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Charikar, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Duchi, J. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Follmer, S. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
James, D. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Kochenderfer, M. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (PI);
Parlante, N. (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);
Saberi, A. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (PI);
Saxena, A. (PI);
Schwarz, K. (PI);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Valiant, G. (PI);
Van Roy, B. (PI);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Williams, V. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Yamins, D. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
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 571:
Surgical Robotics Seminar (ME 571)
Surgical robots developed and implemented clinically on varying scales. Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry. Engineering and clinical aspects connected to design and use of surgical robots, varying in degree of complexity and procedural role. May be repeated for credit.
Terms: Aut
| Units: 1
| Repeatable
for credit
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Aiken, A. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Cain, J. (PI);
Cao, P. (PI);
Cheriton, D. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Liang, P. (PI);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (PI);
Pea, R. (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);
Shoham, Y. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Moreau, D. (GP)
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Aiken, A. (PI);
Akeley, K. (PI);
Altman, R. (PI);
Baker, M. (PI);
Barbagli, F. (PI);
Batzoglou, S. (PI);
Bejerano, G. (PI);
Bernstein, M. (PI);
Blikstein, P. (PI);
Boneh, D. (PI);
Bradski, G. (PI);
Brafman, R. (PI);
Brunskill, E. (PI);
Cain, J. (PI);
Cao, P. (PI);
Casado, M. (PI);
Cheriton, D. (PI);
Cooper, S. (PI);
Dally, B. (PI);
De-Micheli, G. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dwork, C. (PI);
Engler, D. (PI);
Ermon, S. (PI);
Fedkiw, R. (PI);
Feigenbaum, E. (PI);
Fikes, R. (PI);
Fisher, K. (PI);
Fogg, B. (PI);
Fox, A. (PI);
Garcia-Molina, H. (PI);
Genesereth, M. (PI);
Gill, J. (PI);
Girod, B. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Heer, J. (PI);
Hennessy, J. (PI);
Horowitz, M. (PI);
Johari, R. (PI);
Johnson, M. (PI);
Jurafsky, D. (PI);
Katti, S. (PI);
Kay, M. (PI);
Khatib, O. (PI);
Klemmer, S. (PI);
Koller, D. (PI);
Koltun, V. (PI);
Konolige, K. (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);
Mackey, L. (PI);
Manna, Z. (PI);
Manning, C. (PI);
Mazieres, D. (PI);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (PI);
Motwani, R. (PI);
Musen, M. (PI);
Nass, C. (PI);
Nayak, P. (PI);
Ng, A. (PI);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Parlante, N. (PI);
Pea, R. (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);
Shoham, Y. (PI);
Sidford, A. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Trevisan, L. (PI);
Ullman, J. (PI);
Van Roy, B. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Winstein, K. (PI);
Wootters, M. (PI);
Young, P. (PI);
Zaharia, M. (PI);
Zelenski, J. (PI);
Zou, J. (PI);
George, S. (GP);
Moreau, D. (GP)
