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 2C:
Multimedia Production
Sound, image and video editing techniques and applications, including understanding file formats and publishing multimedia online. Topics: GarageBand, Photoshop, iMovie, Final Cut Pro, and iDVD. Weekly lecture followed by lab section. Second unit for additional creative production assignments completed outside of class time and Final Project with group. Not a programming course, but will use computer multimedia applications heavily for editing.
Terms: Aut, Win
| Units: 1-2
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: Win
| Units: 1
CS 56N:
Great Discoveries and Inventions in Computing
This seminar will explore some of both the great discoveries that underlie computer science and the inventions that have produced the remarkable advances in computing technology. Key questions we will explore include: What is computable? How can information be securely communicated? How do computers fundamentally work? What makes computers fast? Our exploration will look both at the principles behind the discoveries and inventions, as well as the history and the people involved in those events. Some exposure to programming will be helpful, but it not strictly necessary.
Terms: Win
| Units: 3
CS 77:
Interaction Design Basics
Reduced version of CS 147, focusing on interaction, not implementation. As an introduction to the methods and principles of designing user interfaces, the course will cover topics such as needfinding, rapid prototyping, visual design, and interface evaluation. In addition to weekly lectures and quizzes, assignments culminate in a final design project consisting of an interactive prototype of a web application. Prerequisites: none.
Terms: Win
| Units: 2
CS 78:
Understanding Women's Experience in High-Tech Companies
Women continue to be underrepresented in high-tech companies at every level and companies have problems with retention. In this course students who will form the next generation in these industries will critically look at the lives and challenges of women in technology. They will explore their personal journey, review classic and recent literature on women and work, and conduct field interviews of women in the industry. The students will work in groups to interpret and organize field data to reveal psychosocial, cultural, and organizational themes that affect women's choices. They generate guiding insights for themselves and organizations to be shared with selected groups at Stanford. Required: declared major in computer science or other major with design coursework. Recommended: CS 147 or equivalent experience with design fieldwork.
Terms: Win
| Units: 3
CS 93SI:
Introduction to Functional Programming in Haskell
A brief introduction to the functional programming paradigm in the Haskell programming language. Recursion, higher-order functions, currying. Lists and list operations. Data types and typeclasses. Functors and Monads.
Terms: Win
| Units: 1
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. May not be taken by students who have completed 103A,B or 103X. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
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, Win
| 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 and requires an application.
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; application required.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
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, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, 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, MATH 51 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.
Terms: Win, Spr
| 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)
Automated processing of less structured information: human language text and speech, web pages, social networks, genome sequences, with goal of automatically extracting meaning and structure. Methods include: string algorithms, automata and transducers, hidden Markov models, graph algorithms, XML processing. Applications such as information retrieval, text classification, social network models, machine translation, genomic sequence alignment, word meaning extraction, and speech recognition. Prerequisite: CS103, CS107, CS109.
Terms: Win
| 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, Spr
| 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, javascript, and the document object model. Server-side technologies such as sessions, templates, relational databases, and object-relational mapping. 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 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.
Terms: Win
| Units: 3-4
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: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
CS 181:
Computers, Ethics, and Public Policy
(Formerly 201.) 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: Aut, 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 and Computer Systems Engineering undergraduates.
Terms: Aut, Win, Spr
| Units: 4
| UG Reqs: GER:EC-EthicReas, WAY-ER
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.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| 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);
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);
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);
Golub, G. (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);
McCarthy, J. (PI);
McCluskey, E. (PI);
McKeown, N. (PI);
Meng, T. (PI);
Mitchell, J. (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);
Pea, R. (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);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Siroker, M. (GP);
Swenson, M. (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.
Terms: Aut, Win, Spr
| Units: 3-6
| 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);
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);
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);
Golub, G. (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);
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);
Pea, R. (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);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (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);
Golub, G. (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);
Hadding, D. (GP);
Swenson, M. (GP)
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. Prerequisites: CS 110 and CS 161.
Terms: Win, Spr
| Units: 3
| Repeatable
for credit
CS 194W:
Software Project (WIM)
Restricted to Computer Science, Computer Systems Engineering, and Electrical Engineering undergraduates. Writing-intensive version of CS194.
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 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: ;
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);
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);
Golub, G. (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);
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);
Pande, V. (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);
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);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (GP)
CS 199P:
Independent Work
(Staff)
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors: ;
Aiken, A. (PI);
Altman, R. (PI);
Baker, M. (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);
Golub, G. (PI);
Goodman, N. (PI);
Guibas, L. (PI);
Hanrahan, P. (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);
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);
Parlante, N. (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);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (GP)
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 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 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 229T:
Statistical Learning Theory (STATS 231)
(Same as 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 online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. 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: Win
| 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: Win
| 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. In the fall and spring quarter, a sequence of interactive web/video modules substitutes the classroom lectures. In the winter quarter, the course is taught conventionally; both versions of the course are equivalent. Recommended: EE261, EE278B.
Terms: Aut, Win, Spr
| 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 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
Distributed file systems: Hadoop, map-reduce; PageRank, topic-sensitive PageRank, spam detection, hubs-and-authorities; similarity search; shingling, minhashing, random hyperplanes, locality-sensitive hashing; analysis of social-network graphs; association rules; dimensionality reduction: UV, SVD, and CUR decompositions; algorithms for very-large-scale mining: clustering, nearest-neighbor search, gradient descent, support-vector machines, classification, and regression; submodular function optimization. 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 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 254:
Computational Complexity
An introduction to computational complexity theory. The P versus NP problem; diagonalization and relativization; space complexity, Savitch's algorithm, NL=coNL, Reingold's algorithm; counting problem and #P-completeness; circuit complexity; pseudorandomness, derandomixation, and the Natural Proofs barrier; complexity of approximation; quantum computing. Prerequisites: 154 or equivalent; mathematical maturity.
Terms: Win
| 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 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 262:
Computational Genomics (BIOMEDIN 262)
Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.
Terms: Win
| 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.
| Units: 3
CS 270:
Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)
Methods for modeling biomedical systems and for making those models explicit in the context of building 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. Recommended: exposure to object-oriented systems, basic biology.
Terms: Win
| Units: 3
CS 275:
Translational Bioinformatics (BIOMEDIN 217)
Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
Terms: 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 331A:
Advanced Reading in Computer Vision
(Formerly CS323) The field of computer vision has seen an explosive growth in past decade. Much of recent effort in vision research is towards developing algorithms that can perform high-level visual recognization tasks on real-world images and videos. With development of Internet, this task becomes particularly challenging and interesting given the heterogeneous data on the web. Course will focus on reading recent research papers that are focused on solving high-level visual recognition problems, such as object recognition and categorization, scene understanding, human motion understanding, etc. Project required. Prerequisite: some experience in research with one of the following fields: computer vision, image processing, computer graphics, machine learning.
Terms: Win
| 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: Win, Sum
| Units: 3
CS 345D:
Topics in Database Systems
The first part of the course will describe classical database systems topics, including join processing, concurrency control, recovery, query optimization, and database theory. On each topic, there will be an in-depth discussion of a few representative papers and recent results. The second part of the course will focus on additional topics that are relevant to database systems, including MapReduce-style processing, information extraction, and predictive analytics. The course readings will primarily consist of classical and recent research papers. Prerequisites: CS 145 or equivalent.
Terms: Win
| Units: 3
| Repeatable
for credit
CS 346:
Database System Implementation
A major database system implementation project realizes the principles and techniques covered in earlier courses. Students independently build a complete database management system, from file structures through query processing, with a personally designed feature or extension. Lectures on project details and advanced techniques in database system implementation, focusing on query processing and optimization. Guest speakers from industry on commercial DBMS implementation techniques. Prerequisites: 145, 245, programming experience in C++.
Terms: Win, Spr
| Units: 3-5
CS 348A:
Computer Graphics: Geometric Modeling
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, Bezier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multiresolution representations of geometry. Representations of solids and conversions among them. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification. Prerequisite: linear algebra. Recommended: 164, 248.
Terms: Win
| Units: 3-4
CS 364B:
Topics in Algorithmic Game Theory
Topics on the interface of computer science and game theory. May be repeated for credit. Prerequisites: 364A or instructor permission.
Terms: Win
| Units: 3
| Repeatable
for credit
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);
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);
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);
Fischer, M. (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);
Golub, G. (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);
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);
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);
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);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (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: ;
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);
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);
Golub, G. (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);
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);
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);
Hadding, D. (GP);
Swenson, M. (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);
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);
Golub, G. (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);
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);
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);
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);
Hadding, D. (GP);
Swenson, M. (GP)
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);
Golub, G. (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);
Hadding, D. (GP);
Swenson, M. (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);
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);
Golub, G. (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);
Hadding, D. (GP);
Swenson, M. (GP)
CS 399:
Independent Project
Letter grade only.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| 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);
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);
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);
Golub, G. (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);
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);
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);
Nilsson, N. (PI);
Olukotun, O. (PI);
Ousterhout, J. (PI);
Paepcke, A. (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);
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);
Wang, G. (PI);
Widom, J. (PI);
Wiederhold, G. (PI);
Williams, R. (PI);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (GP)
CS 399P:
Independent Project
Graded satisfactory/no credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-9
| 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);
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);
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);
Golub, G. (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);
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);
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);
Roberts, E. (PI);
Rosenblum, M. (PI);
Roughgarden, T. (PI);
Sahami, M. (PI);
Salisbury, J. (PI);
Savarese, S. (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);
Winograd, T. (PI);
Young, P. (PI);
Zelenski, J. (PI);
George, S. (GP);
Hadding, D. (GP);
Swenson, M. (GP)
CS 431:
High-Level Vision: Object Representation (PSYCH 250)
(Formerly CS423 High-Level Vision: Behaviors, Neurons, and Computational Models) 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 468:
Topics in Geometric Algorithms:Data-Driven Shape Analysis
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: Win, Spr
| Units: 3
| Repeatable
for credit
CS 476B:
Music, Computing, Design II: Mobile Music (MUSIC 256B)
Aesthetic, design, and implementation of mobile music, centered around the modern super smartphones such as the iPhone). Similarities and intrinsic differences between mobile and traditional computing and design for music. Topics include mobile software design, social and cloud computing, mobile interface design, and programming phones, in the service of music. Prerequisite: MUSIC 256A.
Terms: Win
| Units: 1-4
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: ;
Aiken, A. (PI);
Akeley, K. (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);
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);
Golub, G. (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);
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);
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);
Hadding, D. (GP);
Swenson, M. (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: ;
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);
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);
Golub, G. (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);
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);
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);
Wang, G. (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);
Hadding, D. (GP);
Swenson, M. (GP)
CS 545:
Database and Information Management Seminar
Current research and industrial innovation in database and information systems.
Terms: Win
| Units: 1
| Repeatable
for credit
CS 546:
Seminar on Liberation Technologies (POLISCI 337S)
This one-unit seminar will present speakers relevant in a variety ofnnways to how various forms of information technology are being used tonndefend human rights, improve governance, deepen democracy, empower thennpoor, promote economic development, protect the environment, enhancennpublic health, and pursue a variety of other social goods.
Terms: Aut, Win
| Units: 1
| Repeatable
for credit
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
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);
Golub, G. (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);
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);
Hadding, D. (GP);
Swenson, M. (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);
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);
Golub, G. (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);
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);
Savarese, S. (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);
Hadding, D. (GP);
Swenson, M. (GP)
