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CS 1
Introduction to Computer Programming
9 units (342)

first term
A course on computer programming emphasizing the program design process and pragmatic programming skills. It will use the Python programming language and will not assume previous programming experience. Material covered will include data types, variables, assignment, control structures, functions, scoping, compound data, string processing, modules, basic input/output (terminal and file), as well as more advanced topics such as recursion, exception handling and objectoriented programming. Program development and maintenance skills including debugging, testing, and documentation will also be taught. Assignments will include problems drawn from fields such as graphics, numerics, networking, and games. At the end of the course, students will be ready to learn other programming languages in courses such as CS 11, and will also be ready to take more indepth courses such as CS 2 and CS 4.
Instructor:
Vanier
CS 2
Introduction to Programming Methods
9 units (261)

second term
Prerequisites: CS 1 or equivalent.
CS 2 is a demanding course in programming languages and computer science. Topics covered include data structures, including lists, trees, and graphs; implementation and performance analysis of common algorithms; algorithm design principles, in particular recursion and dynamic programming; concurrency and network programming; basic numerical computation methods. Heavy emphasis is placed on the use of compiled languages and development tools, including source control and debugging. The course includes weekly laboratory exercises and written homework covering the lecture material and program design. The course is intended to establish a foundation for further work in many topics in the computer science option.
Instructors:
Barr, Desbrun
CS 3
Introduction to Software Engineering
9 units (243)

third term
Prerequisites: CS 2 or equivalent.
CS 3 is an advanced introduction to the fundamentals of computer science and software engineering methodology. Topics will be chosen from the following: abstract data types; objectoriented models and methods; logic, specification, and program composition; abstract models of computation; probabilistic algorithms; nondeterminism; distributed algorithms and data structures. The weekly laboratory exercises allow the students to investigate the lecture material by writing nontrivial applications.
Instructor:
Staff
CS 4
Fundamentals of Computer Programming
9 units (342)

second term
Prerequisites: CS 1 or instructor's permission.
This course gives students the conceptual background necessary to construct and analyze programs, which includes specifying computations, understanding evaluation models, and using major programming language constructs (functions and procedures, conditionals, recursion and looping, scoping and environments, compound data, side effects, higherorder functions and functional programming, and objectoriented programming). It emphasizes key issues that arise in programming and in computation in general, including time and space complexity, choice of data representation, and abstraction management. This course is intended for students with some programming background who want a deeper understanding of the conceptual issues involved in computer programming.
Instructor:
Vanier
Ma/CS 6 abc
Introduction to Discrete Mathematics
9 units (306)

first, second, third terms
Prerequisites: for Ma/CS 6 c, Ma/CS 6 a or Ma 5 a or instructor's permission.
First term: a survey emphasizing graph theory, algorithms, and applications of algebraic structures. Graphs: paths, trees, circuits, breadthfirst and depthfirst searches, colorings, matchings. Enumeration techniques; formal power series; combinatorial interpretations. Topics from coding and cryptography, including Hamming codes and RSA. Second term: directed graphs; networks; combinatorial optimization; linear programming. Permutation groups; counting nonisomorphic structures. Topics from extremal graph and set theory, and partially ordered sets. Third term: elements of computability theory and computational complexity. Discussion of the P=NP problem, syntax and semantics of propositional and firstorder logic. Introduction to the Godel completeness and incompleteness theorems.
Instructors:
Wilson, Marx
CS 9
Introduction to Computer Science Research
1 unit (100)

first term
This course will introduce the research areas of the computer science faculty, through weekly overview talks by the faculty aimed at firstyear undergraduates. Others may wish to take the course to gain an understanding of the scope of the field. Graded pass/fail.
Instructor:
Umans
CS 11
Computer Language Shop
3 units (030)

first, second, third terms
Prerequisites: CS 1 or instructor's permission.
A selfpaced lab that provides students with extra practice and supervision in transferring their programming skills to a particular programming language; the course can be used for any language of the student's choosing, subject to approval by the instructor. A series of exercises guide the student through the pragmatic use of the chosen language, building his or her familiarity, experience, and style. More advanced students may propose their own programming project as the target demonstration of their new language skills. CS 11 may be repeated for credit of up to a total of nine units.
Instructors:
Pinkston, Vanier
CS 21
Decidability and Tractability
9 units (306)

second term
Prerequisites: CS 2 (may be taken concurrently).
This course introduces the formal foundations of computer science, the fundamental limits of computation, and the limits of efficient computation. Topics will include automata and Turing machines, decidability and undecidability, reductions between computational problems, and the theory of NPcompleteness.
Instructor:
Umans
CS 24
Introduction to Computing Systems
9 units (333)

third term
Prerequisites: Familiarity with C equivalent to having taken the CS 11 C track.
Basic introduction to computer systems, including hardwaresoftware interface, computer architecture, and operating systems. Course emphasizes computer system abstractions and the hardware and software techniques necessary to support them, including virtualization (e.g., memory, processing, communication), dynamic resource management, and commoncase optimization, isolation, and naming.
Instructor:
Pinkston
CS 38
Introduction to Algorithms
9 units (306)

third term
Prerequisites: CS 2; Ma/CS 6 a or Ma 121 a; and CS 21 or CS/EE/Ma 129 a.
This course introduces techniques for the design and analysis of efficient algorithms. Major design techniques (the greedy approach, divide and conquer, dynamic programming, linear programming) will be introduced through a variety of algebraic, graph, and optimization problems. Methods for identifying intractability (via NPcompleteness) will be discussed.
Instructor:
Umans
EE/CS 51
Principles of Microprocessor Systems
12 units (453)

first term
The principles and design of microprocessorbased computer systems. Lectures cover both hardware and software aspects of microprocessor system design such as interfacing to input and output devices, user interface design, realtime systems, and tabledriven software. The homework emphasis is on software development, especially interfacing with hardware, in assembly language.
Instructor:
George
EE/CS 52
Microprocessor Systems Laboratory
12 units (1110)

second term
Prerequisites: EE/CS 51 or equivalent.
The student will design, build, and program a specified microprocessorbased system. This structured laboratory is organized to familiarize the student with electronic circuit construction techniques, modern development facilities, and standard design techniques. The lectures cover topics in microprocessor system design such as display technologies, interfacing with analog systems, and programming microprocessors in highlevel languages.
Instructor:
George
EE/CS 53
Microprocessor Project Laboratory
12 units (0120)

first, second, third terms
Prerequisites: EE/CS 52 or equivalent.
A project laboratory to permit the student to select, design, and build a microprocessorbased system. The student is expected to take a project from proposal through design and implementation (possibly including PCB fabrication) to final review and documentation. May be repeated for credit.
Instructor:
George
CS/EE/ME 75 abc
Introduction to Multidisciplinary Systems Engineering
3 units (201) , 6 units (204), or 9 units (207) first term

6 units (231), 9 units (261), or 12 units (291) second term
This course presents the fundamentals of modern multidisciplinary systems engineering in the context of a substantial design project. Students from a variety of disciplines will conceive, design, implement, and operate a system involving electrical, information, and mechanical engineering components. Specific tools will be provided for setting project goals and objectives, managing interfaces between component subsystems, working in design teams, and tracking progress against tasks. Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. During the first two terms of the course, students will attend project meetings and learn some basic tools for project design, while taking courses in CS, EE, and ME that are related to the course project. During the third term, the entire team will build, document, and demonstrate the course design project, which will differ from year to year. Freshmen must receive permission from the lead instructor to enroll. Not offered 201314.
CS 80 abc
Undergraduate Thesis
9 units

first, second, third terms
Prerequisites: instructor's permission, which should be obtained sufficiently early to allow time for planning the research.
Individual research project, carried out under the supervision of a member of the computer science faculty (or other faculty as approved by the computer science undergraduate option representative). Projects must include significant design effort. Written report required. Open only to upperclass students. Not offered on a pass/fail basis.
Instructor:
Staff
CS 81 abc
Undergraduate Projects in Computer Science
Units are assigned in accordance with work accomplished
Prerequisites: Consent of supervisor is required before registering.
Supervised research or development in computer science by undergraduates. The topic must be approved by the project supervisor, and a formal final report must be presented on completion of research. This course can (with approval) be used to satisfy the project requirement for the CS major. Graded pass/fail.
Instructor:
Staff
CS 90
Undergraduate Reading in Computer Science
Units are assigned in accordance with work accomplished
Prerequisites: Consent of supervisor is required before registering.
Supervised reading in computer science by undergraduates. The topic must be approved by the reading supervisor, and a formal final report must be presented on completion of the term. Graded pass/fail.
Instructor:
Staff
CS 101 abc
Special Topics in Computer Science
Units in accordance with work accomplished

offered by announcement
Prerequisites: CS 21 and CS 38, or instructor's permission.
The topics covered vary from year to year, depending on the students and staff. Primarily for undergraduates.
ACM/CS 114
Parallel Algorithms for Scientific Applications
9 units (306)

second, third term
Prerequisites: ACM 11, 106 or equivalent.
Introduction to parallel program design for numerically intensive scientific applications. Parallel programming methods; distributedmemory model with message passing using the message passing interface; sharedmemory model with threads using open MP, CUDA; objectbased models using a problemsolving environment with parallel objects. Parallel numerical algorithms: numerical methods for linear algebraic systems, such as LU decomposition, QR method, CG solvers; parallel implementations of numerical methods for PDEs, including finitedifference, finiteelement; particlebased simulations. Performance measurement, scaling and parallel efficiency, load balancing strategies.
Instructor:
Aivazis
CS 115
Functional Programming
9 units (342)

third term
Prerequisites: CS 1 and CS 4.
This course is a both a theoretical and practical introduction to functional programming, a paradigm which allows programmers to work at an extremely high level of abstraction while simultaneously avoiding large classes of bugs that plague more conventional imperative and objectoriented languages. The course will introduce and use the lazy functional language Haskell exclusively. Topics include: recursion, firstclass functions, higherorder functions, algebraic data types, polymorphic types, function composition, pointfree style, proving functions correct, lazy evaluation, pattern matching, lexical scoping, type classes, and modules. Some advanced topics such as monad transformers, parser combinators, dynamic typing, and existential types are also covered.
Instructor:
Vanier
CS 116
Reasoning about Program Correctness
9 units (306)

first term
Prerequisites: CS 1 or equivalent.
This course presents the use of logic and formal reasoning to prove the correctness of sequential and concurrent programs. Topics in logic include propositional logic, basics of firstorder logic, and the use of logic notations for specifying programs. The course presents a programming notation and its formal semantics, Hoare logic and its use in proving program correctness, predicate transformers and weakest preconditions, and fixedpoint theory and its application to proofs of programs.
Instructor:
Josh
Ma/CS 117 abc
Computability Theory
9 units (306)

first, second, third terms
Prerequisites: Ma 5 or equivalent, or instructor's permission.
Various approaches to computability theory, e.g., Turing machines, recursive functions, Markov algorithms
CS 118
Logic Model Checking for Formal Software Verification
9 units (333)

second term
An introduction to the theory and practice of logic model checking as an aid in the formal proofs of correctness of concurrent programs and system designs. The specific focus is on automatatheoretic verification. The course includes a study of the theory underlying formal verification, the correctness of programs, and the use of software tools in designs. Not offered 201314.
CS 119
Reliable Software: Testing and Monitoring
9 units (333)

third term
Prerequisites: CS 1 or equivalent; CS 116 and CS 118 are recommended.
The class discusses theoretical and practical aspects of software testing and monitoring. Topics include finite state machine testing algorithms, random testing, constraintbased testing, coverage measures, automated debugging, logics and algorithms for runtime monitoring, and aspectoriented approaches to monitoring. Emphasis is placed on automation. Students will be expected to develop and use software testing and monitoring tools to develop reliable software systems. Not offered 201314.
CS 121
Introduction to Relational Databases
9 units (306)

first term
Prerequisites: CS 1 or equivalent.
Introduction to the basic theory and usage of relational database systems. It covers the relational data model, relational algebra, and the Structured Query Language (SQL). The course introduces the basics of database schema design and covers the entityrelationship model, functional dependency analysis, and normal forms. Additional topics include other query languages based on the relational calculi, datawarehousing and dimensional analysis, writing and using stored procedures, working with hierarchies and graphs within relational databases, and an overview of transaction processing and query evaluation. Extensive handson work with SQL databases.
Instructor:
Pinkston
CS 122
Database System Implementation
9 units (333)

second term
Prerequisites: CS2, CS38, CS 121 and familiarity with Java, or instructor's permission.
This course explores the theory, algorithms, and approaches behind modern relational database systems. Topics include file storage formats, query planning and optimization, query evaluation, indexes, transaction processing, concurrency control, and recovery. Assignments consist of a series of programming projects extending a working relational database, giving handson experience with the topics covered in class. The course also has a strong focus on proper software engineering practices, including version control, testing, and documentation.
Instructor:
Pinkston
CS 123
Projects in Database Systems
9 units (009)

third term
Prerequisites: CS121 and CS122.
Students are expected to execute a substantial project in databases, write up a report describing their work, and make a presentation.
Instructor:
Pinkston
CS 124
Operating Systems
(306)

Second term
Prerequisites: CS 24.
This course explores the major themes and components of modern operating systems, such as kernel architectures, the process abstraction and process scheduling, system calls, concurrency within the OS, virtual memory management, and file systems. Students must work in groups to complete a series of challenging programming projects, implementing major components of an instructional operating system. Most programming is in C, although some IA32 assembly language programming is also necessary. Familiarity with the material in CS 24 is strongly advised before attempting this course.
Instructor:
Pinkston
EE/Ma/CS 126 ab
Information Theory
9 units (306)

first, second terms
Prerequisites: Ma 2.
Shannon's mathematical theory of communication, 1948present. Entropy, relative entropy, and mutual information for discrete and continuous random variables. Shannon's source and channel coding theorems. Mathematical models for information sources and communication channels, including memoryless, first order Markov, ergodic, and Gaussian. Calculation of capacity and ratedistortion functions. Kolmogorov complexity and universal source codes. Side information in source coding and communications. Network information theory, including multiuser data compression, multiple access channels, broadcast channels, and multiterminal networks. Discussion of philosophical and practical implications of the theory. This course, when combined with EE 112, EE/Ma/CS 127, EE 161, and/or EE 167 should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression.
Instructor:
Effros
EE/Ma/CS 127
ErrorCorrecting Codes
9 units (306)

third term
Prerequisites: Ma 2.
This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission or storage systems. Topics include algebraic block codes, e.g., Hamming, BCH, ReedSolomon (including a selfcontained introduction to the theory of finite fields); and the modern theory of sparse graph codes with iterative decoding, e.g. LDPC codes, turbo codes, fountain coding. Emphasis will be placed on the associated encoding and decoding algorithms, and students will be asked to demonstrate their understanding with a software project.
Instructor:
Ho
CS/EE/Ma 129 abc
Information and Complexity
9 units (306), first and second terms

(144) third term
Prerequisites: basic knowledge of probability and discrete mathematics.
A basic course in information theory and computational complexity with emphasis on fundamental concepts and tools that equip the student for research and provide a foundation for pattern recognition and learning theory. First term: what information is and what computation is; entropy, source coding, Turing machines, uncomputability. Second term: topics in information and complexity; Kolmogorov complexity, channel coding, circuit complexity, NPcompleteness. Third term: theoretical and experimental projects on current research topics. Not offered 201314.
ME/CS 132 ab
Advanced Robotics: Navigation and Vision
9 units (360)

first, second terms
Prerequisites: ME 115 ab.
The course focuses on current topics in robotics research in the area of autonomous navigation and vision. Topics will include mobile robots, multilegged walking machines, use of vision in navigation systems. The lectures will be divided between a review of the appropriate analytical techniques and a survey of the current research literature. Course work will focus on an independent research project chosen by the student. Not offered 201314.
Ec/CS 133
Electricity Markets
9 units (306)

first term
Prerequisites: Ec 11 or Ec 172.
This in depth introductory course provides an overview of the industry focusing on the linkages between power system engineering, markets, and regulatory policy. We will analyze the fundamentals of various electricity markets including locational marginal pricing, bilateral, dayahead, realtime, capacity, emissions markets and risk markets. We will identify the basic components, design, and operation of electric power systems. We will examine how markets should be designed to be consistent with the engineering fundamentals of electric power systems. We will discuss sensors, metering devices, communication, and computation required to enable markets to functions.
Instructor:
Ledyard
EE/CS/EST 135
Power System Analysis
9 units (306)

second term
Prerequisites: EE 44, Ma 2a, or equivalent.
Phasor representation, 3phase transmission system, perphase analysis; power system modeling, transmission line, transformer, generator; network matrix, power flow solution, optimal power flow; Swing equation, stability, protection; demand response, power markets.
Instructor:
Low
CS 138 abc
Computer Algorithms
9 units (306)

first, second, third terms
Prerequisites: CS 21 and CS 38, or instructor's permission.
Design and analysis of algorithms. Techniques for problems concerning graphs, flows, number theory, string matching, data compression, geometry, linear algebra and coding theory. Optimization, including linear programming. Randomization. Basic complexity theory and cryptography. Not offered 201314.
CS 139 abc
Concurrency in Computation
9 units (306)

first, second, third terms
Prerequisites: CS 21 and CS 38, or instructor's permission.
Design and verification of concurrent algorithms. Topics: different models of concurrent computations; process synchronization by shared variables and synchronization primitives; distributed processes communicating by message exchange; the concepts of synchronization, indivisible actions, deadlock, and fairness; semantics and correctness proofs; implementation issues; and application to VLSI algorithm design. Parallel machine architecture issues include mapping a parallel algorithm on a network of processors, and classical parallel algorithms and their complexity. Not offered 201314.
CS 142
Distributed Computing
(306)

third term
Prerequisites: CS 3, CS 24, CS 38.
Programming distributed systems. Mechanics for cooperation among concurrent agents. Programming sensor networks and cloud computing applications. Applications of machine learning and statistics by using parallel computers to aggregate and analyze data streams from sensors.
Instructor:
Chandy
CS/EE 143
Communication Networks
9 units (333)

first term
Prerequisites: Ma 2, Ma 3, CS 24 and CS 38, or instructor permission.
This course introduces the basic mechanisms and protocols in communication networks, and mathematical models for their analysis. It covers topics such as digitization, switching, switch design, routing, error control (ARQ), congestion control, layering, queuing models, optimization models, basics of protocols in the Internet, wireless networks, and optical networks.
Instructor:
Low
CS/EE 144
The Ideas Behind Our Networked World
12 units (336)

second term
Prerequisites: Ma 2, Ma 3, CS 24 and CS 38, or instructor permission.
Social networks, the web, and the Internet are an essential parts of our lives and we all depend on them every day, but do you really know what makes them work? This course studies the "big" ideas behind our networked lives. Things like, what do networks actually look like (and why do they all look the same)? How do search engines work? Why do memes spread the way they do? How does web advertising work? For all these questions and more, the course will provide a mixture of both mathematical analysis and handson labs. This course can be combined with CS/EE 145 and CS 142 or CS/EE 143 to satisfy the project requirement for CS undergraduate degree, but CS/EE 143 and CS 142 are not required prerequisites. The course assumes students are comfortable with graph theory, probability, and basic programming.
Instructor:
Wierman
CS/EE 145
Projects in Networking
9 units (009)

third term
Prerequisites: Either CS/EE 144 or CS 141 b in the preceding term, or instructor permission.
Students are expected to execute a substantial project in networking, write up a report describing their work, and make a presentation.
Instructors:
Low, Wierman
CS/EE 146
Advanced Networking
9 units (333)

third term
Prerequisites: CS/EE 143 or instructor's permission.
This is a researchoriented course meant for undergraduates and beginning graduate students who want to learn about current research topics in networks such as the Internet, power networks, social networks, etc. The topics covered in the course will vary, but will be pulled from current research topics in the design, analysis, control, and optimization of networks, protocols, and Internet applications. Usually offered in alternate years.
Instructor:
Wierman
CS/EE 147
Network Performance Analysis
9 units (306)

third term
Prerequisites: Ma 2, Ma 3 is required. CS/EE 143, CS/EE 144, and ACM 116 are recommended.
When designing a network protocol, distributed system, etc., it is essential to be able to quantify the performance impacts of design choices along the way. For example, should we invest in more buffer space or a faster processor? One fast disk or multiple slower disks? How should requests be scheduled? What dispatching policy will work best? Ideally, one would like to make these choices before investing the time and money to build a system. This class will teach students how to answer this type of "what if" question by introducing students to analytic performance modeling, the tools necessary for rigorous system design. The course will focus on the mathematical tools of performance analysis (which include stochastic modeling, scheduling theory, and queueing theory) but will also highlight applications of these tools to real systems. Usually offered in alternate years. Not offered 201314.
EE/CNS/CS 148 ab
Selected Topics in Computational Vision
9 units (306)

first, third terms
Prerequisites: undergraduate calculus, linear algebra, geometry, statistics, computer programming. EE 148a is not a prerequisite for EE 148b.
The class will focus on an advanced topic in computational vision: recognition, visionbased navigation, 3D reconstruction. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system. Part a not offered 201314; Part b offered 201314.
Instructor:
Perona
SS/CS 149
Introduction to Algorithmic Economics
9 units (306)

first term
Prerequisites: Ma 3, CS 24 and CS 38, or instructor permission.
This course will equip students to engage with current topics of active research at the intersection of social and information sciences, including: algorithmic mechanism design; auctions; existence and computation of equilibria; and learning and games.
Instructor:
Ligett
CS 150
Probability and Algorithms
9 units (306)

second term
Prerequisites: CS 38 a and Ma 5 abc.
Elementary randomized algorithms and algebraic bounds in communication, hashing, and identity testing. Game tree evaluation. Topics may include randomized parallel computation; independence, kwise independence and derandomization; rapidly mixing Markov chains; expander graphs and their applications; clustering algorithms. Not offered 201314.
CS 151
Complexity Theory
12 units (309)

third term
Prerequisites: CS 21 and CS 38, or instructor's permission.
This course describes a diverse array of complexity classes that are used to classify problems according to the computational resources (such as time, space, randomness, or parallelism) required for their solution. The course examines problems whose fundamental nature is exposed by this framework, the known relationships between complexity classes, and the numerous open problems in the area. Not offered 201314.
CS/SS 152
Introduction to Data Privacy
9 units (306)

first term
Prerequisites: Ma 3, CS 24 and CS 38, or instructor's permission.
How should we define privacy? What are the tradeoffs between useful computation on large datasets and the privacy of those from whom the data is derived? This course will take a mathematically rigorous approach to addressing these and other questions at the frontier of research in data privacy. We will draw connections with a wide variety of topics, including economics, statistics, information theory, game theory, probability, learning theory, geometry, and approximation algorithms. Not offered 201314.
CS 153
Current Topics in Theoretical Computer Science
9 units (306)

second term
Prerequisites: CS 21 and CS 38, or instructor's permission.
May be repeated for credit, with permission of the instructor. Students in this course will study an area of current interest in theoretical computer science. The lectures will cover relevant background material at an advanced level and present results from selected recent papers within that year's chosen theme. Students will be expected to read and present a research paper. Not offered 201314.
CS/CNS/EE/NB 154
Artificial Intelligence
9 units (333)

first term
Prerequisites: Ma 2 b or equivalent, and CS 1 or equivalent.
How can we build systems that perform well in unk nown environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upperlevel undergraduate and graduate students. Not offered 201314.
CS/CNS/EE 155
Probabilistic Graphical Models
9 units (333)

second term
Prerequisites: background in algorithms and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156 a or instructor's permission).
Many realworld problems in AI, computer vision, robotics, computer systems, computational neuroscience, computational biology, and natural language processing require one to reason about highly uncertain, structured data, and draw global insight from local observations. Probabilistic graphical models allow addressing these challenges in a unified framework. These models generalize approaches such as hidden Markov models and Kalman filters, factor analysis, and Markov random fields. In this course, we will study the problem of learning such models from data, performing inference (both exact and approximate), and using these models for making decisions. The techniques draw from statistics, algorithms, and discrete and convex optimization. The course will be heavily research oriented, covering current developments such as probabilistic relational models, models for naturally combining logical and probabilistic inference, and nonparametric Bayesian methods. Not offered 201314.
CS/CNS/EE 156 ab
Learning Systems
9 units (306)

first, third terms
Prerequisites: Ma 2 and CS 2, or equivalent.
Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks.
Instructor:
AbuMostafa
CS/CNS/EE 159
Projects in Machine Learning and AI
9 units (009)

third term
Prerequisites: Two terms from the "Learning & Vision" project sequence.
Students are expected to execute a substantial project in AI and/or machine learning, write up a report describing their work, and make a presentation. Not offered 201314.
CS/CNS 171
Introduction to Computer Graphics Laboratory
12 units (363)

first term
Prerequisites: Ma 2 and extensive programming experience.
This course introduces the basic ideas behind computer graphics and its fundamental algorithms. Topics include graphics input and output, the graphics pipeline, sampling and image manipulation, threedimensional transformations and interactive modeling, basics of physically based modeling and animation, simple shading models and their hardware implementation, and fundamental algorithms of scientific visualization. Students will be required to perform significant implementations.
Instructor:
Barr
CS/CNS 174
Computer Graphics Projects
12 units (363)

third term
Prerequisites: Ma 2 and CS/CNS 171 or instructor's permission.
This laboratory class offers students an opportunity for independent work covering recent computer graphics research. In coordination with the instructor, students select a computer graphics modeling, rendering, interaction, or related algorithm and implement it. Students are required to present their work in class and discuss the results of their implementation and any possible improvements to the basic methods. May be repeated for credit with instructor's permission.
Instructor:
Barr
CS 176
Introduction to Computer Graphics Research
9 units (333)

second term
Prerequisites: CS/CNS 171, or 173, or 174.
The course will go over recent research results in computer graphics, covering subjects from mesh processing (acquisition, compression, smoothing, parameterization, adaptive meshing), simulation for purposes of animation, rendering (both photo and nonphotorealistic), geometric modeling primitives (image based, point based), and motion capture and editing. Other subjects may be treated as they appear in the recent literature. The goal of the course is to bring students up to the frontiers of computer graphics research and prepare them for their own research.
Instructor:
Staff
CS 177
Discrete Differential Geometry: Theory and Applications
9 units (333)

first term
Topics include, but are not limited to, discrete exterior calculus; Whitney forms; DeRham and Whitney complexes; Morse theory; computational and algebraic topology; discrete simulation of thin shells, fluids, electromagnetism, elasticity; surface parameterization; Hodge decomposition.
Instructor:
SchrÃ¶der
CS 179
GPU Programming
9 units (333)

third term
Prerequisites: Working knowledge of C. Some experience with computer graphics algorithms preferred, but not required.
The use of Graphics Processing Units for computer graphics rendering is well known, but their power for general parallel computation is only recently being explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms. This course covers programming techniques for the Graphics processing unit, focusing on visualization and simulation of various systems. Labs will cover specific applications in graphics, mechanics, and signal processing. The course will introduce the OpenGL Shader Language (GLSL) and nVidia's parallel computing architecture, CUDA. Labwork will require extensive programming.
Instructor:
Desbrun
CS/EE 181 abc
VLSI Design Laboratory
12 units (363)

first, second terms
Digital integrated system design, with projects involving the design, verification, and testing of highcomplexity CMOS microcircuits. Firstterm lecture and homework topics emphasize disciplined design, and include CMOS logic, layout, and timing; computeraided design and analysis tools; and electrical and performance considerations. Each student is required in the first term to complete individually the design, layout, and verification of a moderately complex integrated circuit. Advanced topics second and third terms include selftimed design, computer architecture, and other topics that vary year by year. Projects are largescale designs done by teams. Part c not offered 201314.
Instructor:
Martin
CS 185 abc
Asynchronous VLSI Design Laboratory
9 units (333)

first, second, third terms
Prerequisites: CS 139.
The design of digital integrated circuits whose correct operation is independent of delays in wires and gates. (Such circuits do not use clocks.) Emphasis is placed on highlevel synthesis, design by program transformations, and correctness by construction. The first term introduces delayinsensitive design techniques, description of circuits as concurrent programs, circuit compilation, standardcell layout and other computeraided design tools, and electrical optimizations. The second term is reserved for advanced topics, and for the presentation and review of midsize projects, which will be fabricated in CMOS or GaAs technologies, and tested. Not offered 201314.
CNS/Bi/EE/CS/NB 186
Vision: From Computational Theory to Neuronal Mechanisms
12 units (444)

second term
Lecture, laboratory, and project course aimed at understanding visual information processing, in both machines and the mammalian visual system. The course will emphasize an interdisciplinary approach aimed at understanding vision at several levels: computational theory, algorithms, psychophysics, and hardware (i.e., neuroanatomy and neurophysiology of the mammalian visual system). The course will focus on early vision processes, in particular motion analysis, binocular stereo, brightness, color and texture analysis, visual attention and boundary detection. Students will be required to hand in approximately three homework assignments as well as complete one project integrating aspects of mathematical analysis, modeling, physiology, psychophysics, and engineering. Given in alternate years; offered 201314.
CNS/Bi/Ph/CS/NB 187
Neural Computation
9 units (306)

first term
Prerequisites: familiarity with digital circuits, probability theory, linear algebra, and differential equations. Programming will be required.
This course investigates computation by neurons. Of primary concern are models of neural computation and their neurological substrate, as well as the physics of collective computation. Thus, neurobiology is used as a motivating factor to introduce the relevant algorithms. Topics include ratecode neural networks, their differential equations, and equivalent circuits; stochastic models and their energy functions; associative memory; supervised and unsupervised learning; development; spikebased computing; singlecell computation; error and noise tolerance.
Instructor:
Perona
BE/CS/CNS/Bi 191 ab
Biomolecular Computation
9 units (306) second term

(243) third term
Prerequisites: none. Recommended: ChE/BE 163, CS 21, CS 129 ab, or equivalent.
This course investigates computation by molecular systems, emphasizing models of computation based on the underlying physics, chemistry, and organization of biological cells. We will explore programmability, complexity, simulation of and reasoning about abstract models of chemical reaction networks, molecular folding, molecular selfassembly, and molecular motors, with an emphasis on universal architectures for computation, control, and construction within molecular systems. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton; physical limits of computation, reversibility, reliability, and the role of noise, DNAbased computers and DNA nanotechnology. Part a develops fundamental results; part b is a reading and research course: classic and current papers will be discussed, and students will do projects on current research topics.
Instructor:
Winfree
ACM/CS/EE 218
Statistical Inference
306

third term
Prerequisites: ACM 104 and ACM 116, or instructor's permission.
Fundamentals of estimation theory and hypothesis testing; Bayesian and nonBayesian approaches; minimax analysis, CramerRao bounds, shrinkage in high dimensions; Kalman filtering, basics of graphical models; statistical model selection. Throughout the course, a computational viewpoint will be emphasized.
Instructor:
Chandrasekaran
Ph/CS 219 abc
Quantum Computation
9 units (306)

first, second terms
Prerequisites: Ph 129 abc or equivalent.
The theory of quantum information and quantum computation. Overview of classical information theory, compression of quantum information, transmission of quantum information through noisy channels, quantum errorcorrecting codes, quantum cryptography and teleportation. Overview of classical complexity theory, quantum complexity, efficient quantum algorithms, faulttolerant quantum computation, physical implementations of quantum computation.
Instructors:
Preskill, Kitae
SS/CS 241
Topics in Algorithmic Economics
9 units (306)
Prerequisites: SS/CS 149.
This is a graduatelevel seminar covering recent topics at the intersection of computer science and economics. Topics will vary, but may include, e.g., dynamics in games, algorithmic mechanism design, and prediction markets. Not offered 201314.
Instructor:
EAS and HSS faculty
CS/CNS/EE 253
Special Topics in Machine Learning
9 units (333)
Prerequisites: CS/CNS/EE/NB 154 or CS/CNS/EE 156 a or instructor's permission.
This course is an advanced, researchoriented seminar in machine learning and AI meant for graduate students and advanced undergraduates. The topics covered in the course will vary, but will always come from the cutting edge of machine learning and AI research. Examples of possible topics are active learning and optimized information gathering, AI in distributed systems, computational learning theory, machine learning applications (on the Web, in sensor networks and robotics). Not offered 201314.
CS 274 abc
Topics in Computer Graphics
9 units (333)

first, second, third terms
Prerequisites: instructor's permission.
Each term will focus on some topic in computer graphics, such as geometric modeling, rendering, animation, humancomputer interaction, or mathematical foundations. The topics will vary from year to year. May be repeated for credit with instructor's permission. Not offered 201314.
Published Date:
July 28, 2022