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 Computer Science Courses



Lower Division Courses

3L.  Introduction to Symbolic Programming. (4)   Students may remove a deficiency in 3 by taking 3L. One hour of lecture and six hours of laboratory per week and approximately five hours of self-scheduled programming laboratory. Prerequisites: High school algebra. Introduction to computer programming, emphasizing symbolic computation and functional programming style. Students will write a project of at least 200 lines of code in Scheme (a dialect of the LISP programming language). (F,SP) Clancy

3S.  Introduction to Symbolic Programming (Self-Paced). (1-4)   Refer to computer science service course restrictions. Course may be repeated up to 4 units. One to four hours of discussion and three to nine hours of laboratory per week. Prerequisites: High school algebra. The same material as 3 but in a self-paced format; introduction to computer programming, emphasizing symbolic computation and functional programming style, using the Scheme programming language. Units assigned depend on amount of work completed. The first two units must be taken together. (F,SP) Garcia

9A.  Matlab for Programmers. (2)   Refer to computer science service course restrictions. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience equivalent to that gained in Computer Science 10; familiarity with applications of matrix processing. Introduction to the constructs in the Matlab programming language, aimed at students who already know how to program. Array and matrix operations, functions and function handles, control flow, plotting and image manipulation, cell arrays and structures, and the Symbolic Mathematics toolbox. (F,SP) Garcia

9C.  C for Programmers. (2)   Refer to computer science service course restrictions. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience with pointers (or addresses in assembly language) and linked data structures equivalent to that gained in Computer Science 9B or 61A, or Engineering 7. Self-paced course in the C programming language for students who already know how to program. Computation, input and output, flow of control, functions, arrays, and pointers, linked structures, use of dynamic storage, and implementation of abstract data types. (F,SP) Garcia

9D.  Scheme and Functional Programming for Programmers. (2)   Refer to computer science service course restrictions. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience similar to that gained in Computer Science 10 or Engineering 7. Self-paced course in functional programming, using the Scheme programming language, for students who already know how to program. Recursion; higher-order functions; list processing; implementation of rule-based querying. (F,SP) Garcia

9E.  Productive Use of the UNIX Environment. (2)   Refer to computer science service course restrictions. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience similar to that gained in Computer Science 61A or Engineering 7; DOS or UNIX experience. Use of UNIX utilities and scripting facilities for customizing the programming environment, organizing files (possibly in more than one computer account), implementing a personal database, reformatting text, and searching for online resources. (F,SP) Garcia

9F.  C++ for Programmers. (2)   Refer to computer science service course restrictions in the General Catalog. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience equivalent to that gained in Computer Science 9B or 61A, or Engineering 7. Self-paced introduction to the constructs provided in the C++ programming language for procedural and object-oriented programming, aimed at students who already know how to program. (F,SP) Garcia

9G.  JAVA for Programmers. (2)   Self-paced. Must be taken on a passed/not passed basis. Prerequisites: 9C or 9F or 61A plus experience with object-oriented programming or C-based language. Self-paced course in Java for students who already know how to program. Applets; variables and computation; events and flow of control; classes and objects; inheritance; GUI elements; applications; arrays, strings, files, and linked structures; exceptions; threads. (F,SP) Garcia

9H.  Python for Programmers. (2)   Refer to computer science service course restrictions. Self-paced. Must be taken on a passed/not passed basis. Prerequisites: Programming experience equivalent to that gained in Computer Science 10. Introduction to the constructs provided in the Python programming language, aimed at students who already know how to program. Flow of control; strings, tuples, lists, and dictionaries; CGI programming; file input and output; object-oriented programming; GUI elements. (F,SP) Garcia

10.  The Beauty and Joy of Computing. (4)   Two hours of lecture, four hours of laboratory, and one hour of discussion per week. An introduction to the beauty and joy of computing. The history, social implications, great principles, and future of computing. Beautiful applications that have changed the world. How computing empowers discovery and progress in other fields. Relevance of computing to the student and society will be emphasized. Students will learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests. (F,SP) Garcia, Harvey

W10.  The Beauty and Joy of Computing. (4)   Students will receive no credit for W10 after taking 10. A deficient grade in 10 may be removed by taking W10. Two hours of web-based lecture, four hours of web-based laboratory, and one hour of web-based discussion per week. This course meets the programming prerequisite for 61A. An introduction to the beauty and joy of computing. The history, social implications, great principles, and future of computing. Beautiful applications that have changed the world. How computing empowers discovery and progress in other fields. Relevance of computing to the student and society will be emphasized. Students will learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests. (F,SP) Garcia, Harvey

24.  Freshman Seminars. (1)   Course may be repeated for credit as topic varies. One hour of seminar per week. Sections 1-2 to be graded on a letter-grade basis. Sections 3-4 to be graded on a passed/not passed basis. The Freshman Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Freshman seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Enrollment limited to 15 freshmen. (F,SP)

39.  Freshman/Sophomore Seminar.   Course may be repeated for credit as topic varies. One hour of seminar per week per unit. Sections 1-2 to be graded on a letter-grade basis. Sections 3-4 to be graded on a passed/not passed basis. Prerequisites: Priority given to freshmen and sophomores. Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25. (F,SP)

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47A.  Completion of Work in Computer Science 61A. (1)   Students will receive no credit for 47A after taking 61A. Self-paced. Prerequisites: 61B or equivalent, 9D, and consent of instructor. Implementation of generic operations. Streams and iterators. Implementation techniques for supporting functional, object-oriented, and constraint-based programming in the Scheme programming language. Together with 9D, 47A constitutes an abbreviated, self-paced version of 61A for students who have already taken a course equivalent to 61B. (F,SP) Garcia

47B.  Completion of Work in Computer Science 61B. (1)   Students will receive no credit for 47B after taking 61B. Self-paced. Prerequisites: A course in data structures, 9G or equivalent, and consent of instructor. Iterators. Hashing, applied to strings and multi-dimensional structures. Heaps. Storage management. Design and implementation of a program containing hundreds of lines of code. Students with sufficient partial credit in 61B may, with consent of instructor, complete the credit in this self-paced course. (F,SP) Garcia

47C.  Completion of Work in Computer Science 61C. (1)   Students will receive no credit for 47C after taking 61C. Self-paced. Prerequisites: Experience with assembly language including writing an interrupt handler, 9C or equivalent, and consent of instructor. MIPS instruction set simulation. The assembly and linking process. Caches and virtual memory. Pipelined computer organization. Students with sufficient partial credit in 61C may, with consent of instructor, complete the credit in this self-paced course. (F,SP) Garcia

61A.  The Structure and Interpretation of Computer Programs. (4)   Students will receive no credit for Computer Science 61A after completing Computer Science 47A or Computer Science 61AS. A deficient grade in Computer Science 61AS may be removed by taking Computer Science 61A. Three hours of lecture, one and one-half hours of laboratory, and one and one-half hours of discussion per week. Prerequisites: Mathematics 1A (may be taken concurrently); programming experience equivalent to that gained in 3 or the Advanced Placement Computer Science A course. Introduction to programming and computer science. This course exposes students to techniques of abstraction at several levels: (a) within a programming language, using higher-order functions, manifest types, data-directed programming, and message-passing; (b) between programming languages, using functional and rule-based languages as examples. It also relates these techniques to the practical problems of implementation of languages and algorithms on a von Neumann machine. There are several significant programming projects. (F,SP) Garcia, Hilfinger

61AS.  The Structure and Interpretation of Computer Programs (Self-Paced). (1-4)   Course may be repeated for a maximum of 4 units. Students will receive no credit for Computer Science 61AS after completing Computer Science 47A or Computer Science 61A. A deficient grade in Computer Science 61A may be removed by taking Computer Science 61AS. Six hours of laboratory per week. Prerequisites: Mathematics 1A (may be taken concurrently). Programming experience equivalent to that gained in 10 or the Advanced Placement Computer Science A course is recommended, but is not essential; students without this experience will begin at an earlier point in the online course. Introductory programming and computer science. Abstraction as means to control program complexity. Programming paradigms: functional, object-oriented, client/server, and declarative (logic). Control abstraction: recursion and higher order functions. Introduction to asymptotic analysis of algorithms. Data abstraction: abstract data types, type-tagged data, first class data types, sequences implemented as lists and as arrays, generic operators implemented with data-directed programming and with message passing. Implementation of object-oriented programming with closures over dispatch procedures. Introduction to interpreters and compilers. There are several significant programming projects. Course may be completed in one or two semesters. Students must complete a mimimum of two units during their first semester of 61AS. (F,SP) Garcia, Harvey, Hilfinger

61B.  Data Structures. (4)   Students will receive no credit for 61B after taking 47B or 61BL. Deficiency in 61BL may be removed by taking 61B. Three hours of lecture, one hour of discussion, and two hours of laboratory per week. Prerequisites: 61A or Engineering 7. Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language. (F,SP) Hilfinger, Shewchuk

61BL.  Data Structures and Programming Methodology. (4)   Students will receive no credit for 61BL after taking 47B or 61B. Deficiency in 61B may be removed by taking 61BL. One hour of lecture and six hours of laboratory per week. Prerequisites: 61A or Engineering 7. The same material as in 61B, but in a laboratory-based format. (F,SP) Hilfinger

61C.  Machine Structures. (4)   Students will receive no credit for 61C after taking 47C or 61CL. Deficiency in 61C may be removed by taking 61CL. Three hours of lecture, two hours of laboratory, and one hour of discussion per week. Prerequisites: 61A, along with either 61B or 61BL, or programming experience equivalent to that gained in 9C, 9F, or 9G. The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions. (F,SP) Garcia, Franklin, Katz, Patterson

61CL.  Machine Structures (Lab-Centric). (4)   Students will receive no credit for 61CL after taking 47C or 61C. Deficiency in 61C may be removed by taking 61CL. Two hours of lecture, one hour of discussion, and four hours of laboratory per week. Prerequisites: 61A, along with 61B or 61BL, or programming experience equivalent to that gained in 9C, 9F, or 9G . The same material as in 61C but in a lab-centric format. (F,SP) Garcia, Patterson

70.  Discrete Mathematics and Probability Theory. (4)   Students will receive no credit for 70 after taking Mathematics 55. Three hours of lecture per week, or three hours of lecture and two hours of discussion per week. Prerequisites: Sophomore mathematical maturity, and programming experience equivalent to that gained in 3 or the Advanced Placement Computer Science A course. Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. (F,SP) Papadimitriou, Rao, Sinclair, Trevisan, Vazirani, Wagner

C79.  Societal Risks and the Law. (3)   Three hours of lecture and one hour of discussion per week. Prerequisites: One semester of calculus. Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering. Also listed as Political Science C79 and Statistics C79. (F,SP) Sekhon, Stark, Wagner, Staff

97.  Field Study. (1-4)   Course may be repeated for credit. One to four hours of fieldwork per week. Must be taken on a passed/not passed basis. Prerequisites: Consent of instructor (see department adviser). Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation. (F,SP) Staff

98.  Directed Group Study. (1-4)   Course may be repeated for credit. One hour of lecture per week per unit. Must be taken on a passed/not passed basis. Prerequisites: Consent of instructor. Seminars for group study of selected topics, which will vary from year to year. Intended for students in the lower division. (F,SP) Staff

99.  Individual Study and Research for Undergraduates. (1-2)   Course may be repeated for credit. Must be taken on a passed/not passed basis. Prerequisites: GPA of 3.4 or better. A course for lower division students in good standing who wish to undertake a program of individual inquiry initiated jointly by the student and a professor. There are no other formal prerequisites, but the supervising professor must be convinced that the student is able to profit by the program. (F,SP) Staff

Upper Division Courses

C149.  Introduction to Embedded Systems. (4)   Students will receive no credit for Electrical Engineering C149/Computer Science C149 after taking Electrical Engineering C249M/Computer Science C249M. Students may remove a deficient grade in Electrical Engineering C149/Computer Science C149 after taking Electrical Engineering 124. Three hours of lecture and three hours of laboratory per week. Prerequisites: 20N; Computer Science 61C; Computer Science 70 or Math 55. This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects. Also listed as Electrical Engineering C149. (F,SP) Lee, Seshia

150.  Components and Design Techniques for Digital Systems. (5)   Three hours of lecture, three hours of laboratory, and one hour of discussion per week. Prerequisites: Computer Science 61C, Electrical Engineering 40. Basic building blocks and design methods to contruct synchronous digital systems, such as general purpose processors, hardware accelerators, and application specific processors. Representations and design methodologies for digital systems. Logic design using combinatorial and sequential circuits. Digital system implementation considering hardware descriptions languages, computer-aided design tools, field-programmable gate array architectures, and CMOS logic gates and state elements. Interfaces between peripherals, processor hardware, and software. Formal hardware laboratories and substantial design project. (F,SP) Katz, Pister, Wawrzynek

152.  Computer Architecture and Engineering. (4)   Three hours of lecture and two hours of discussion per week. Prerequisites: 61C. Instruction set architecture, microcoding, pipelining (simple and complex). Memory hierarchies and virtual memory. Processor parallelism: VLIW, vectors, multithreading. Multiprocessors. (F,SP) Asanovic, Culler, Kubiatowicz, Wawrzynek

160.  User Interface Design and Development. (4)   Students will receive no credit for Computer Science 160 after taking Computer Science 260A. Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B or 61BL. The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams. (F,SP) Agrawala, Canny, Hartmann

161.  Computer Security. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: 61C (Machine Structures), plus either 70 (Discrete Mathematics) or Mathematics 55. Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and language-based security. Case studies from real-world systems. (F,SP) Paxson, Song, Tygar, Wagner

162.  Operating Systems and System Programming. (4)   Three hours of lecture, four hours of laboratory, and one hour of discussion per week. Prerequisites: Computer Science 61B, 61C, and 70. Basic concepts of operating systems and system programming. Utility programs, subsystems, multiple-program systems. Processes, interprocess communication, and synchronization. Memory allocation, segmentation, paging. Loading and linking, libraries. Resource allocation, scheduling, performance evaluation. File systems, storage devices, I/O systems. Protection, security, and privacy. (F,SP) Joseph, Kubiatowicz

164.  Programming Languages and Compilers. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: 61B and 61C. Survey of programming languages. The design of modern programming languages. Principles and techniques of scanning, parsing, semantic analysis, and code generation. Implementation of compilers, interpreters, and assemblers. Overview of run-time organization and error handling. (F,SP) Bodik, Hilfinger, Necula

168.  Introduction to the Internet: Architecture and Protocols. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B and 162. This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless). (F,SP) Katz, Paxson, Ratnasamy, Shenker, Stoica

169.  Software Engineering. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B and 61C, and either Computer Science 70 or Mathematics 113. Ideas and techniques for designing, developing, and modifying large software systems. Function-oriented and object-oriented modular design techniques, designing for re-use and maintainability. Specification and documentation. Verification and validation. Cost and quality metrics and estimation. Project team organization and management. Students will work in teams on a substantial programming project. (F,SP) Brewer, Fox, Necula, Sen

170.  Efficient Algorithms and Intractable Problems. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B and 70. Concept and basic techniques in the design and analysis of algorithms; models of computation; lower bounds; algorithms for optimum search trees, balanced trees and UNION-FIND algorithms; numerical and algebraic algorithms; combinatorial algorithms. Turing machines, how to count steps, deterministic and nondeterministic Turing machines, NP-completeness. Unsolvable and intractable problems. (F,SP) Demmel, Papadimitriou, Rao, Wagner, Vazirani

172.  Computability and Complexity. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: 170. Finite automata, Turing machines and RAMs. Undecidable, exponential, and polynomial-time problems. Polynomial-time equivalence of all reasonable models of computation. Nondeterministic Turing machines. Theory of NP-completeness: Cook's theorem, NP-completeness of basic problems. Selected topics in language theory, complexity and randomness. (F,SP) Papadimitriou, Seshia, Sinclair, Vazirani

174.  Combinatorics and Discrete Probability. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: 170. Permutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychev's inequality, Chernov bounds. Birthday paradox, coupon collector's problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence bounds. (F,SP) Bartlett, Papadimitriou, Sinclair, Vazirani

176.  Algorithms for Computational Biology. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 70 and 170. Experience programming in a language such as C, C++, Java, or Python. Algorithms and probabilistic models that arise in various computational biology applications: suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, genome rearrangements, hidden Markov models, gene finding, motif finding, stochastic context free grammars, RNA secondary structure. There are no biology prerequisites for this course, but a strong quantitative background will be essential. (F,SP) Song

184.  Foundations of Computer Graphics. (4)   Students will receive no credit for Comp Sci 184 after taking Comp Sci 284A. Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B or 61BL; programming skills in C, C++, or Java; linear algebra and calculus. Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Algorithms for clipping, hidden surface removal, rasterization, and anti-aliasing. Scan-line based and ray-based rendering algorithms. Lighting models for reflection, refraction, transparency. (F,SP) O'Brien, Sequin, Barsky, Ramamoorthi, Agrawala

186.  Introduction to Database Systems. (4)   Students will receive no credit for Comp Sci 186 after taking Comp Sci 286A. Three hours of lecture and one hour of discussion per week. Prerequisites: 61B and 61C. Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project. (F,SP) Franklin, Hellerstein

188.  Introduction to Artificial Intelligence. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61A; Computer Science 61B; Computer Science 70. Ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception, and language understanding. (F,SP) Abbeel, Klein, Russell

189.  Introduction to Machine Learning. (4)   Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A. Three hours of lecture and one hour of discussion per week. Prerequisites: Mathematics 53 and 54; Computer Science 70; Computer Science 188 or consent of instructor. Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications. (F,SP) Abbeel, Bartlett, Darrell, El Ghaoui, Jordan, Klein, Malik, Russell

C191.  Quantum Information Science and Technology. (3)   Three hours of lecture/discussion per week. Prerequisites: Mathematics 54, Physics 7A-7B, and either Physics 7C, Mathematics 55, or Computer Science 170. This multidisciplinary course provides an introduction to fundamental conceptual aspects of quantum mechanics from a computational and informational theoretic perspective, as well as physical implementations and technological applications of quantum information science. Basic sections of quantum algorithms, complexity, and cryptography, will be touched upon, as well as pertinent physical realizations from nanoscale science and engineering. Also listed as Physics C191 and Chemistry C191. (F,SP) Crommie, Vazirani, Whaley

194.  Special Topics. (1-4)   Course may be repeated for credit as topic varies. One to fours hours of lecture/discussion per week. Prerequisites: Consent of instructor. Topics will vary semester to semester. See the Computer Science Division announcements. (F,SP) Staff

195.  Social Implications of Computer Technology. (1)   Students will receive no credit for 195 after taking C195/Interdisciplinary Field Study C155 or H195. One and one-half hours of lecture per week. Must be taken on a passed/not passed basis. Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students will lead discussions on additional topics. (F,SP) Harvey

H195.  Honors Social Implications of Computer Technology. (3)   Student will receive no credit for H195 after taking 195 or C195. One and one-half hours of lecture and one and one-half hours of discussion per week. Must be taken on a passed/not passed basis. Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students may lead discussions on additional topics. (F,SP) Harvey

H196.  Honors Seminar for Computer Science Majors. (3)   Three hours of lecture per week and project work. Must be taken on a passed/not passed basis. Prerequisites: 150, 170, and consent of instructor. Study in-depth of several topics in computer science to be chosen by the instructor. Students will assess current literature in the topics and present critiques to the class. Each student will carry out a project. (SP) Staff

H196A-H196B.  Senior Honors Thesis Research. (1-4;1-4)   Individual research. Prerequisites: Open only to students in the computer science honors program. Thesis work under the supervision of a faculty member. To obtain credit the student must, at the end of two semesters, submit a satisfactory thesis to the Electrical Engineering and Computer Science department archive. A total of four units must be taken. The units many be distributed between one or two semesters in any way. H196A-H196B count as graded technical elective units, but may not be used to satisfy the requirement for 27 upper division technical units in the College of Letters and Science with a major in Computer Science. (F,SP)

197.  Field Study. (1-4)   Course may be repeated for credit. One to four hours of fieldwork per week. Must be taken on a passed/not passed basis. Prerequisites: Consent of instructor (see department adviser). Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation. (F,SP) Staff

198.  Directed Group Studies for Advanced Undergraduates. (1-4)   Course may be repeated for credit. Course format varies with section. Must be taken on a passed/not passed basis. Prerequisites: 2.0 GPA or better; 60 units completed. Group study of selected topics in Computer Sciences, usually relating to new developments.

199.  Supervised Independent Study. (1-4)   Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. Individual conferences. Must be taken on a passed/not passed basis. Prerequisites: Consent of instructor and major adviser. Supervised independent study. Enrollment restrictions apply. (F,SP) Staff

Graduate Courses

C219D.  Concurrent Models of Computation. (3)   Course may be repeated for credit with consent of instructor. Three hours of lecture per week. Prerequisites: Graduate standing. Theory and practice of concurrent models of computation (MoCs) with applications to software systems, embedded systems, and cyber-physical systems. Analysis for boundedness, deadlock, and determinacy; formal semantics (fixed point semantics and metric-space models); composition; heterogeneity; and model-based design. MoCs covered may include process networks, threads, message passing, synchronous/reactive, dataflow, rendezvous, time-triggered, discrete events, and continuous time. Also listed as Electrical Engineering C219D. (F,SP) Lee

C249A.  Introduction to Embedded Systems. (4)   Students will receive no credit for El Eng/Comp Sci C249A after taking El Eng/Comp Sci C149. Three hours of lecture and three hours of laboratory per week. Formerly Electrical Engineering C249M/Computer Science C249M. This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects. Also listed as Electrical Engineering C249A. (F,SP) Lee, Seshia

250.  VLSI Systems Design. (4)   Three hours of lecture and four hours design laboratory per week. Prerequisites: 150. Unified top-down and bottom-up design of integrated circuits and systems concentrating on architectural and topological issues. VLSI architectures, systolic arrays, self-timed systems. Trends in VLSI development. Physical limits. Tradeoffs in custom-design, standard cells, gate arrays. VLSI design tools. (F) Wawrzynek

252.  Graduate Computer Architecture. (4)   Three hours of lecture and one hour of discussion per week. Prerequisites: 152. Graduate survey of contemporary computer organizations covering: early systems, CPU design, instruction sets, control, processors, busses, ALU, memory, I/O interfaces, connection networks, virtual memory, pipelined computers, multiprocessors, and case studies. Term paper or project is required. (F,SP) Culler, Kubiatowicz, Patterson

260A.  User Interface Design and Development. (4)   Students will receive no credit for Computer Science 260A after taking Computer Science 160. Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B, 61BL, or consent of instructor. The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams. (F,SP) Agrawala, Canny, Hartmann

260B.  Human-Computer Interaction Research. (3)   Three hours of lecture per week. Prerequisites: Computer Science 160 recommended, or consent of instructor. This course is a broad introduction to conducting research in Human-Computer Interaction. Students will become familiar with seminal and recent literature; learn to review and critique research papers; re-implement and evaluate important existing systems; and gain experience in conducting research. Topics include input devices, computer-supported cooperative work, crowdsourcing, design tools, evaluation methods, search and mobile interfaces, usable security, help and tutorial systems. (F,SP) Hartmann

261.  Security in Computer Systems. (3)   Three hours of lecture per week. Prerequisites: 162. Graduate survey of modern topics in computer security, including protection, access control, distributed access security, firewalls, secure coding practices, safe languages, mobile code, and case studies from real-world systems. May also cover cryptographic protocols, privacy and anonymity, and/or other topics as time permits. (SP) D. Song, Wagner

261N.  Internet and Network Security. (4)   Three hours of lecture per week. Prerequisites: Electrical Engineering 122 or equivalent; Computer Science 161 or familiarity with basic security concepts. Develops a thorough grounding in Internet and network security suitable for those interested in conducting research in the area or those more broadly interested in security or networking. Potential topics include denial-of-service; capabilities; network intrusion detection/prevention; worms; forensics; scanning; traffic analysis; legal issues; web attacks; anonymity; wireless and networked devices; honeypots; botnets; scams; underground economy; attacker infrastructure; research pitfalls. (F,SP) Paxson

262A.  Advanced Topics in Computer Systems. (4)   Three hours of lecture per week. Prerequisites: 162 and entrance exam. Formerly 262. Graduate survey of systems for managing computation and information, covering a breadth of topics: early systems; volatile memory management, including virtual memory and buffer management; persistent memory systems, including both file systems and transactional storage managers; storage metadata, physical vs. logical naming, schemas, process scheduling, threading and concurrency control; system support for networking, including remote procedure calls, transactional RPC, TCP, and active messages; security infrastructure; extensible systems and APIs; performance analysis and engineering of large software systems. Homework assignments, exam, and term paper or project required. (F,SP) Brewer, Hellerstein

262B.  Advanced Topics in Computer Systems. (3)   Three hours of lecture per week. Prerequisites: 262A. Continued graduate survey of large-scale systems for managing information and computation. Topics include basic performance measurement; extensibility, with attention to protection, security, and management of abstract data types; index structures, including support for concurrency and recovery; parallelism, including parallel architectures, query processing and scheduling; distributed data management, including distributed and mobile file systems and databases; distributed caching; large-scale data analysis and search. Homework assignments, exam, and term paper or project required. (F,SP) Brewer, Culler, Hellerstein, Joseph

263.  Design of Programming Languages. (3)   Three hours of lecture and one hour of discussion per week. Prerequisites: 164. Selected topics from: analysis, comparison, and design of programming languages, formal description of syntax and semantics, advanced programming techniques, structured programming, debugging, verification of programs and compilers, and proofs of correctness. Necula

264.  Implementation of Programming Languages. (4)   Three hours of lecture, one hour of discussion, and six hours programing laboratory per week. Prerequisites: 164, 263 recommended. Compiler construction. Lexical analysis, syntax analysis. Semantic analysis code generation and optimization. Storage management. Run-time organization. Bodik

265.  Compiler Optimization and Code Generation. (3)   Three hours of lecture per week. Prerequisites: 164. Table-driven and retargetable code generators. Register management. Flow analysis and global optimization methods. Code optimization for advanced languages and architectures. Local code improvement. Optimization by program transformation. Selected additional topics. A term paper or project is required. Sen

266.  Introduction to System Performance Analysis. (3)   Three hours of lecture per week. Prerequisites: 162 and Statistics 5. Formerly 267 and 268. Performance indices. Evaluation techniques. Measurement: instrumentation, design of experiments, interpretation of results. Simulation modeling: simulator design, model calibration, statistical analysis of output data. Introduction to analytic modeling. Workload characterization. Tuning, procurement, and capacity planning application. Program performance evaluation. File and I/O system optimization. CPU Scheduling and architecture performance analysis.

C267.  Applications of Parallel Computers. (3)   Three hours of lecture and one hour of laboratory per week. Models for parallel programming. Fundamental algorithms for linear algebra, sorting, FFT, etc. Survey of parallel machines and machine structures. Exiting parallel programming languages, vectorizing compilers, environments, libraries and toolboxes. Data partitioning techniques. Techniques for synchronization and load balancing. Detailed study and algorithm/program development of medium sized applications. Also listed as Engineering C233. Demmel, Yelick

268.  Computer Networks. (3)   Three hours of lecture per week. Prerequisites: 162. Formerly 292V. Distributed systems, their notivations, applications, and organization. The network component. Network architectures. Local and long-haul networks, technologies, and topologies. Data link, network, and transport protocols. Point-to-point and broadcast networks. Routing and congestion control. Higher-level protocols. Naming. Internetworking. Examples and case studies. Joseph, Katz, Stoica

270.  Combinatorial Algorithms and Data Structures. (3)   Three hours of lecture and one hour of discussion per week. Prerequisites: 170. Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems. Papadimitriou, Rao, Sinclair, Vazirani

271.  Randomness and Computation. (3)   Three hours of lecture per week. Prerequisites: 170 and at least one course numbered 270-279. Computational applications of randomness and computational theories of randomness. Approximate counting and uniform generation of combinatorial objects, rapid convergence of random walks on expander graphs, explicit construction of expander graphs, randomized reductions, Kolmogorov complexity, pseudo-random number generation, semi-random sources. Sinclair

273.  Foundations of Parallel Computation. (3)   Three hours of lecture per week. Prerequisites: 170, or consent of instructor. Formerly 292K. Fundamental theoretical issues in designing parallel algorithms and architectures. Shared memory models of parallel computation. Parallel algorithms for linear algegra, sorting, Fourier Transform, recurrence evaluation, and graph problems. Interconnection network based models. Algorithm design techniques for networks like hypercubes, shuffle-exchanges, threes, meshes and butterfly networks. Systolic arrays and techniques for generating them. Message routing. Rao

274.  Computational Geometry. (3)   Course may be repeated for credit. Three hours of lecture per week. Prerequisites: 170 or equivalent. Formerly 292T. Constructive problems in computational geometry: convex hulls, triangulations, Voronoi diagrams, arrangements of hyperplanes; relationships among these problems. Search problems: advanced data structures; subdivision search; various kinds of range searches. Models of computation; lower bounds. Shewchuk

276.  Cryptography. (3)   Three hours of lecture per week. Prerequisites: 170. Graduate survey of modern topics on theory, foundations, and applications of modern cryptography. One-way functions; pseudorandomness; encryption; authentication; public-key cryptosystems; notions of security. May also cover zero-knowledge proofs, multi-party cryptographic protocols, practical applications, and/or other topics, as time permits. (F,SP) Trevisan, Wagner

278.  Machine-Based Complexity Theory. (3)   Three hours of lecture per week. Prerequisites: 170. Properties of abstract complexity measures; Determinism vs. nondeterminism; time vs. space; complexity hierarchies; aspects of the P-NP question; relative power of various abstract machines. Trevisan

C280.  Computer Vision. (3)   Three hours of lecture per week. Prerequisites: Knowledge of linear algebra and calculus. Mathematics 1A-1B, 53, 54 or equivalent. Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition. Also listed as Vision Science C280. Malik

C281A.  Statistical Learning Theory. (3)   Three hours of lecture per week. Prerequisites: Linear algebra, calculus, basic probability, and statistics, algorithms. Computer Science 289 recommended. Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Also listed as Statistics C241A. (F) Bartlett, Jordan, Wainwright

C281B.  Advanced Topics in Learning and Decision Making. (3)   Three hours of lecture per week. Prerequisites: Computer Science C281A/Statistics C241A. Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Also listed as Statistics C241B. (SP) Bartlett, Jordan, Wainwright

283B.  Computer-Aided Geometric Design and Modeling. (3)   Three hours of lecture per week. Prerequisites: Mathematical skill in calculus and linear algebra. Formerly Computer Science 284. Mathematical techniques for curve and surface representation, including: Hermite interpolation, interpolatory splines, tensed splines, Bezier curves and surfaces, B-splines, Beta-splines, Coons patches, tensor product forms, as well as subdivision end/bounding conditions, and computational considerations. (F,SP) Barsky, Sequin

284A.  Foundations of Computer Graphics. (4)   Students will receive no credit for Computer Science 284A after taking 184. Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B or 61BL; programming skills in C, C++, or Java; linear algebra and calculus; or consent of instructor. Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. (F,SP) Agrawala, Barsky, O'Brien, Ramamoorthi, Sequin

284B.  Advanced Computer Graphics Algorithms and Techniques. (4)   Three hours of lecture per week. Prerequisites: 184 or equivalent. Formerly Computer Science 283. This course provides a graduate-level introduction to advanced computer graphics algorithms and techniques. Students should already be familiar with basic concepts such as transformations, scan-conversion, scene graphs, shading, and light transport. Topics covered in this course include global illumination, mesh processing, subdivision surfaces, basic differential geometry, physically based animation, inverse kinematics, imaging and computational photography, and precomputed light transport. (F,SP) O'Brien, Ramamoorthi

285.  Solid Free-Form Modeling and Fabrication. (3)   Three hours of lecture per week. Prerequisites: 184. From shape design to computer-based descriptions suitable for manufacturing or rapid prototyping. Solid modeling techniques and procedural shape generation. Effective data structures and unambiguous part description formats. Algorithms for dealing with Boolean operations and for machine tool path planning. Problems of finite-precision geometry and machining tolerances. Introduction to some rapid prototyping techniques based on Solid Free-Form Fabrication and NC machining. Other advanced topics and recent developments in the field. Sequin

286A.  Introduction to Database Systems. (4)   Students will receive no credit for CS 286A after taking CS 186. Three hours of lecture and one hour of discussion per week. Prerequisites: Computer Science 61B and 61C. Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project. (F,SP) Franklin, Hellerstein

286B.  Implementation of Data Base Systems. (3)   Three hours of lecture per week. Prerequisites: Computer Science 162 and 186 or 286A . Implementation of data base systems on modern hardware systems. Considerations concerning operating system design, including buffering, page size, prefetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems. Implementation of distributed data bases and data base machines. (F,SP) Franklin, Hellerstein

287.  Advanced Robotics. (3)   Three hours of lecture per week. Prerequisites: Electrical Engineering 125. Advanced topics related to current research in robotics. Planning and control issues for realistic robot systems, taking into account: dynamic constraints, control and sensing uncertainty, and non-holonomic motion constraints. Analysis of friction for assembly and grasping tasks. Sensing systems for hands including tactile and force sensing. Environmental perception from sparse sensors for dextrous hands. Grasp planning and manipulation. Abbeel

288.  Natural Language Processing. (4)   Three hours of lecture per week. Prerequisites: CS188 required, CS170 recommended. Methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering, and computational linguistics techniques. (F,SP) Klein

289A.  Introduction to Machine Learning. (4)   Students will receive no credit for Comp Sci 289A after taking Comp Sci 189. Three hours of lecture and one hour of discussion per week. Prerequisites: Mathematics 53, 54; Computer Science 70; Computer Science 188 or consent of instructor. This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming language. (F,SP) Abbeel, Bartlett, Darrell, El Ghaoui, Jordan, Klein, Malik, Russell

294.  Special Topics. (1-4)   Course may be repeated for credit. Topics will vary from semester to semester. See Computer Science Division announcements. (F,SP) Staff

C294P.  Interactive Device Design. (3)   Three hours of lecture per week. Prerequisites: Instructor consent. This course teaches concepts and skills required to design, prototype, and fabricate interactive devices -- that is, physical objects that intelligently respond to user input and enable new types of interactions. Also listed as Mechanical Engineering C290U. (F,SP) Hartmann, Wright

297.  Field Studies in Computer Science. (1-12)   Course may be repeated for credit. Independent study. Must be taken on a satisfactory/unsatisfactory basis. Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering and/or computer science. Written report required at the end of the semester. (F,SP)

298.  Group Studies Seminars, or Group Research. (1-4)   Course may be repeated for credit. One to four hours per unit. Sections 1-25 to be graded on a satisfactory/unsatisfactory basis. Sections 26-35 to be graded on a letter-grade basis. Advanced study in various subjects through seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. (F,SP) Staff

299.  Individual Research. (1-12)   Course may be repeated for credit. Must be taken on a satisfactory/unsatisfactory basis. Investigations of problems in computer science. (F,SP) Staff

602.  Individual Study for Doctoral Students. (1-8)   Course may be repeated for credit. Course does not satisfy unit or residence requirements for doctoral degree. Independent study, consultation with faculty member. Must be taken on a satisfactory/unsatisfactory basis. Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). (F,SP) Staff

Professional Courses

300.  Teaching Practice. (1-6)   Course may be repeated for credit. Three to twenty hours of discussion and consulting per week. Must be taken on a satisfactory/unsatisfactory basis. Supervised teaching practice, in either a one-on-one tutorial or classroom discussion setting. (F,SP) Staff

302.  Designing Computer Science Education. (3)   Two hours of lecture per week. Prerequisites: Computer Science 301 and two semesters of GSI experience. Discussion and review of research and practice relating to the teaching of computer science: knowledge organization and misconceptions, curriculum and topic organization, evaluation, collaborative learning, technology use, and administrative issues. As part of a semester-long project to design a computer science course, participants invent and refine a variety of homework and exam activities, and evaluate alternatives for textbooks, grading and other administrative policies, and innovative uses of technology. (SP) Garcia

375.  Teaching Techniques for Computer Science. (2)   Course may be repeated for credit. Three hours of discussion for ten weeks. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Consent of instructor. Discussion and practice of techniques for effective teaching, focusing on issues most relevant to teaching assistants in computer science courses. (F,SP) Barsky, Garcia, Harvey

399.  Professional Preparation: Supervised Teaching of Computer Science. (1,2)   Course may be repeated for credit. One hour of meeting with instructor plus 10 hours (1 unit) or 20 hours (2 units) of teaching per week. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Appointment as graduate student instructor. Discussion, problem review and development, guidance of computer science laboratory sections, course development, supervised practice teaching. (F,SP) Staff

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