# Computer Science (CS) Graduate Courses (2020-21)

CS 101.
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.

CS 102 abc.
Seminar in Computer Science.
3, 6, or 9 units as arranged with the instructor:
.
Instructor's permission required.

CS 103 abc.
Reading in Computer Science.
3, 6, or 9 units as arranged with the instructor:
.
Instructor's permission required.

HPS/Pl/CS 110.
Causation and Explanation.
9 units (3-0-6):
second term.
An examination of theories of causation and explanation in philosophy and neighboring disciplines. Topics discussed may include probabilistic and counterfactual treatments of causation, the role of statistical evidence and experimentation in causal inference, and the deductive-nomological model of explanation. The treatment of these topics by important figures from the history of philosophy such as Aristotle, Descartes, and Hume may also be considered.
Instructor: Eberhardt.

CS 111.
Graduate Programming Practicum.
3 units (0-3-0):
first, second terms.
Prerequisites: CS 1 or equivalent.
A self-paced 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. This course is available for graduate students only. CS 111 may be repeated for credit of up to a total of nine units. Undergraduates should register for CS 11.
Instructors: Blank, Vanier.

Ec/ACM/CS 112.
Bayesian Statistics.
9 units (3-0-6):
second term.
Prerequisites: Ma 3, ACM/EE/IDS 116 or equivalent.
This course provides an introduction to Bayesian Statistics and its applications to data analysis in various fields. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. The course combines an introduction to basic theory with a hands-on emphasis on learning how to use these methods in practice so that students can apply them in their own work. Previous familiarity with frequentist statistics is useful but not required.
Instructor: Rangel.

CS 115.
Functional Programming.
9 units (3-4-2):
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 object-oriented languages. The course will introduce and use the lazy functional language Haskell exclusively. Topics include: recursion, first-class functions, higher-order functions, algebraic data types, polymorphic types, function composition, point-free 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 (3-0-6):
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 first-order 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 fixed-point theory and its application to proofs of programs. Not offered 2020-21.

Ma/CS 117 abc.
Computability Theory.
9 units (3-0-6):
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; proof of their equivalence. Church's thesis. Theory of computable functions and effectively enumerable sets. Decision problems. Undecidable problems: word problems for groups, solvability of Diophantine equations (Hilbert's 10th problem). Relations with mathematical logic and the Gödel incompleteness theorems. Decidable problems, from number theory, algebra, combinatorics, and logic. Complexity of decision procedures. Inherently complex problems of exponential and superexponential difficulty. Feasible (polynomial time) computations. Polynomial deterministic vs. nondeterministic algorithms, NP-complete problems and the P = NP question.
Instructors: Kechris, Vidnyanszky.

CS 118.
Logic Model Checking for Formal Software Verification.
9 units (3-3-3):
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 automata-theoretic 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 2020-21.

EE/CS 119 abc.
Advanced Digital Systems Design.
9 units (3-3-3):
first, second terms.
Prerequisites: EE/CS 10 a or CS 24.
Advanced digital design as it applies to the design of systems using PLDs and ASICs (in particular, gate arrays and standard cells). The course covers both design and implementation details of various systems and logic device technologies. The emphasis is on the practical aspects of ASIC design, such as timing, testing, and fault grading. Topics include synchronous design, state machine design, ALU and CPU design, application-specific parallel computer design, design for testability, PALs, FPGAs, VHDL, standard cells, timing analysis, fault vectors, and fault grading. Students are expected to design and implement both systems discussed in the class as well as self-proposed systems using a variety of technologies and tools. Given in alternate years; Offered 2020-21.
Instructor: George.

CS/Ph 120.
Quantum Cryptography.
9 units (3-0-6):
first term.
Prerequisites: Ma 1b, Ph 2b or Ph 12b, CS 21, CS 38 or equivalent recommended (or instructor's permission).
This course is an introduction to quantum cryptography: how to use quantum effects, such as quantum entanglement and uncertainty, to implement cryptographic tasks with levels of security that are impossible to achieve classically. The course covers the fundamental ideas of quantum information that form the basis for quantum cryptography, such as entanglement and quantifying quantum knowledge. We will introduce the security definition for quantum key distribution and see protocols and proofs of security for this task. We will also discuss the basics of device-independent quantum cryptography as well as other cryptographic tasks and protocols, such as bit commitment or position-based cryptography. Not offered 2020-21.

CS/IDS 121.
Relational Databases.
9 units (3-0-6):
second 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 entity-relationship model, functional dependency analysis, and normal forms. Additional topics include other query languages based on the relational calculi, data-warehousing 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 hands-on work with SQL databases.
Instructor: Hovik.

CS 122.
Database System Implementation.
9 units (3-3-3):
second term.
Prerequisites: CS 2, CS 38, CS/IDS 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 hands-on 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. Not offered 2020-21.

CS 123.
Projects in Database Systems.
9 units (0-0-9):
third term.
Prerequisites: CS/IDS 121 and CS 122.
Students are expected to execute a substantial project in databases, write up a report describing their work, and make a presentation. Not offered 2020-21.

CS 124.
Operating Systems.
12 units (3-6-3):
third 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/CS/MedE 125.
Digital Electronics and Design with FPGAs and VHDL.
9 units (3-6-0):
third term.
Prerequisites: basic knowledge of digital electronics.
Study of programmable logic devices (CPLDs and FPGAs). Detailed study of the VHDL language, with basic and advanced applications. Review and discussion of digital design principles for combinational-logic, combinational-arithmetic, sequential, and state-machine circuits. Detailed tutorials for synthesis and simulation tools using FPGAs and VHDL. Wide selection of complete, real-world fundamental advanced projects, including theory, design, simulation, and physical implementation. All designs are implemented using state-of-the-art development boards. Offered 2020-21.
Instructor: Pedroni.

EE/Ma/CS 126 ab.
Information Theory.
9 units (3-0-6):
first, second terms.
Prerequisites: Ma 3.
Shannon's mathematical theory of communication, 1948-present. 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, Markov, ergodic, and Gaussian. Calculation of capacity and rate-distortion functions. 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/IDS 127, EE/CS 161, and EE/CS/IDS 167, should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression.
Instructor: Effros.

EE/Ma/CS/IDS 127.
Error-Correcting Codes.
9 units (3-0-6):
second 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, Reed-Solomon (including a self-contained introduction to the theory of finite fields); and the modern theory of sparse graph codes with iterative decoding, e.g. LDPC codes, turbo codes. The students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes. Not Offered 2020-21.
Instructor: Kostina.

ME/CS/EE 129.
Experimental Robotics.
9 units (3-6-0):
first term.
This course covers the foundations of experimental realization on robotic systems. This includes software infrastructures, e.g., robotic operating systems (ROS), sensor integration, and implementation on hardware platforms. The ideas developed will be integrated onto robotic systems and tested experimentally in the context of class projects. Not offered 2020-2021.

CS 130.
Software Engineering.
9 units (3-3-3):
second and fourth terms.
Prerequisites: CS 2 or equivalent.
This course presents a survey of software engineering principles relevant to all aspects of the software development lifecycle. Students will examine industry best practices in the areas of software specification, development, project management, testing, and release management, including a review of the relevant research literature. Assignments give students the opportunity to explore these topics in depth. Programming assignments use Python and Git, and students should be familiar with Python at a CS1 level, and Git at a CS2/CS3 level, before taking the course.
Instructor: Pinkston.

CS 131.
Programming Languages.
9 units (3-0-6):
third term.
Prerequisites: CS 4. CS 131 is a course on programming languages and their implementation.
It teaches students how to program in a number of simplified languages representing the major programming paradigms in use today (imperative, object-oriented, and functional). It will also teach students how to build and modify the implementations of these languages. Emphasis will not be on syntax or parsing but on the essential differences in these languages and their implementations. Both dynamically-typed and statically-typed languages will be implemented. Relevant theory will be covered as needed. Implementations will mostly be interpreters, but some features of compilers will be covered if time permits. Enrollment limited to 30 students.
Instructor: Vanier.

ME/CS/EE 133 abc.
Robotics.
9 units (3-3-3):
first, second, third terms.
Prerequisites: ME/CS/EE 129, may be taken concurrently, or with permission of instructor.
The course develops the core concepts of robotics. The first quarter focuses on classical robotic manipulation, including topics in rigid body kinematics and dynamics. It develops planar and 3D kinematic formulations and algorithms for forward and inverse computations, Jacobians, and manipulability. The second quarter transitions to planning, navigation, and perception. Topics include configuration space, sample-based planners, A* and D* algorithms, to achieve collision-free motions. The third quarter discusses advanced material, for example grasping and dexterous manipulation using multi-fingered hands, or autonomous behaviors, or human-robot interactions. The lectures will review appropriate analytical techniques and may survey the current research literature. Course work will focus on an independent research project chosen by the student.
Instructor: Niemeyer.

ME/CS/EE 134.
Robotic Systems.
9 units (3-6-0):
second term.
Prerequisites: ME/CS/EE 129, may be taken concurrently, or with permission of instructor.
This course builds up, and brings to practice, the elements of robotic systems at the intersection of hardware, kinematics and control, computer vision, and autonomous behaviors. It presents selected topics from these domains, focusing on their integration into a full sense-think-act robot. The lectures will drive team-based projects, progressing from building custom robots to writing software and implementing all necessary aspects. Working systems will autonomously operate and complete their tasks during final demonstrations.
Instructor: Niemeyer.

EE/CS/EST 135.
Power System Analysis.
9 units (3-3-3):
first term.
Prerequisites: EE 44, Ma 2, or equivalent.
Basic power system analysis: phasor representation, 3-phase transmission system, transmission line models, transformer models, per-unit analysis, network matrix, power flow equations, power flow algorithms, optimal powerflow (OPF) problems, swing dynamics and stability. Current research topics such as (may vary each year): convex relaxation of OPF, frequency regulation, energy functions and contraction regions, volt/var control, storage optimization, electric vehicles charging, demand response.
Instructor: Low.

EE/Ma/CS/IDS 136.
Topics in Information Theory.
9 units (3-0-6):
third term.
Prerequisites: Ma 3 or ACM/EE/IDS 116 or CMS 117 or Ma/ACM/IDS 140a.
This class introduces information measures such as entropy, information divergence, mutual information, information density from a probabilistic point of view, and discusses the relations of those quantities to problems in data compression and transmission, statistical inference, language modeling, game theory and control. Topics include information projection, data processing inequalities, sufficient statistics, hypothesis testing, single-shot approach in information theory, large deviations.
Instructor: Kostina.

CS 137.
Algorithms in the Real World.
12 units (2-9-1):
third term.
Prerequisites: CS 2, CS 24, Ma 6 or permission from instructor.
This course introduces algorithms in the context of their usage in the real world. The course covers compression, advanced data structures, numerical algorithms, cryptography, computer algebra, and parallelism. The goal of the course is for students to see how to use theoretical algorithms in real-world contexts, focusing both on correctness and the nitty-gritty details and optimizations. Implementations focus on two orthogonal avenues: speed (for which C is used) and algorithmic thinking (for which Python is used).
Instructor: Blank.

CS 138.
Computer Algorithms.
9 units (3-0-6):
third term.
This course is identical to CS 38. Only graduate students for whom this is the first algorithms course are allowed to register for CS 138. See the CS 38 entry for prerequisites and course description.
Instructor: Schröder.

CMS/CS/IDS 139.
Analysis and Design of Algorithms.
12 units (3-0-9):
second term.
Prerequisites: Ma 2, Ma 3, Ma/CS 6a, CS 21, CS 38/138, and ACM/EE/IDS 116 or CMS/ACM/IDS 113 or equivalent.
This course develops core principles for the analysis and design of algorithms. Basic material includes mathematical techniques for analyzing performance in terms of resources, such as time, space, and randomness. The course introduces the major paradigms for algorithm design, including greedy methods, divide-and-conquer, dynamic programming, linear and semidefinite programming, randomized algorithms, and online learning.
Instructor: Mahadev.

CS 141.
Hack Society: Projects from the Public Sector.
9 units (0-0-9):
third term.
Prerequisites: CS/IDS 142, 143, CMS/CS/EE/IDS 144, or permission from instructor.
There is a large gap between the public and private sectors' effective use of technology. This gap presents an opportunity for the development of innovative solutions to problems faced by society. Students will develop technology-based projects that address this gap. Course material will offer an introduction to the design, development, and analysis of digital technology with examples derived from services typically found in the public sector.
Instructor: Ralph.

CS/IDS 142.
Distributed Computing.
9 units (3-2-4):
first term.
Prerequisites: 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. Not offered 2020-21.

CS/EE/IDS 143.
Communication Networks.
9 units (3-3-3):
first term.
Prerequisites: Ma 2, Ma 3, CS 24 and CS 38, or instructor permission.
This course focuses on the link layer (two) through the transport layer (four) of Internet protocols. It has two distinct components, analytical and systems. In the analytical part, after a quick summary of basic mechanisms on the Internet, we will focus on congestion control and explain: (1) How to model congestion control algorithms? (2) Is the model well defined? (3) How to characterize the equilibrium points of the model? (4) How to prove the stability of the equilibrium points? We will study basic results in ordinary differential equations, convex optimization, Lyapunov stability theorems, passivity theorems, gradient descent, contraction mapping, and Nyquist stability theory. We will apply these results to prove equilibrium and stability properties of the congestion control models and explore their practical implications. In the systems part, the students will build a software simulator of Internet routing and congestion control algorithms. The goal is not only to expose students to basic analytical tools that are applicable beyond congestion control, but also to demonstrate in depth the entire process of understanding a physical system, building mathematical models of the system, analyzing the models, exploring the practical implications of the analysis, and using the insights to improve the design.
Instructors: Low, Ralph.

CMS/CS/EE/IDS 144.
Networks: Structure & Economics.
12 units (3-4-5):
second term.
Prerequisites: Ma 2, Ma 3, Ma/CS 6 a, and CS 38, or instructor permission.
Social networks, the web, and the internet are essential parts of our lives, and we depend on them every day. This course studies how they work and the "big" ideas behind our networked lives. Questions explored include: 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 hands-on labs. The course expects students to be comfortable with graph theory, probability, and basic programming.
Instructor: Wierman.

CS/EE 145.
Projects in Networking.
9 units (0-0-9):
third term.
Prerequisites: Either CMS/CS/EE/IDS 144 or CS/IDS 142 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.
Instructor: Wierman.

CS/EE 146.
Control and Optimization of Networks.
9 units (3-3-3):
first term.
Prerequisites: Ma 2, Ma 3 or instructor's permission.
This is a research-oriented 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 in the design, analysis, control, and optimization of networks. Usually offered in odd years. Not offered 2020-21.

EE/CS 147.
Digital Ventures Design.
9 units (3-3-3):
first term.
Prerequisites: none.
This course aims to offer the scientific foundations of analysis, design, development, and launching of innovative digital products and study elements of their success and failure. The course provides students with an opportunity to experience combined team-based design, engineering, and entrepreneurship. The lectures present a disciplined step-by-step approach to develop new ventures based on technological innovation in this space, and with invited speakers, cover topics such as market analysis, user/product interaction and design, core competency and competitive position, customer acquisition, business model design, unit economics and viability, and product planning. Throughout the term students will work within an interdisciplinary team of their peers to conceive an innovative digital product concept and produce a business plan and a working prototype. The course project culminates in a public presentation and a final report. Every year the course and projects focus on a particular emerging technology theme. Not offered 2020-21.
Instructor: Staff.

EE/CNS/CS 148.
Selected Topics in Computational Vision.
9 units (3-0-6):
third term.
Prerequisites: undergraduate calculus, linear algebra, geometry, statistics, computer programming.
The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D 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.
Instructor: Perona.

CS/Ec 149.
Algorithmic Economics.
9 units (3-0-6):
second term.
This course will equip students to engage with active research at the intersection of social and information sciences, including: algorithmic game theory and mechanism design; auctions; matching markets; and learning in games.
Instructor: Echenique.

CS/IDS 150 ab.
Probability and Algorithms.
9 units (3-0-6):
first and third terms.
Prerequisites: part a: CS 38 and Ma 5 abc; part b: part a or another introductory course in discrete probability.
Part a: The probabilistic method and randomized algorithms. Deviation bounds, k-wise independence, graph problems, identity testing, derandomization and parallelization, metric space embeddings, local lemma. Part b: Further topics such as weighted sampling, epsilon-biased sample spaces, advanced deviation inequalities, rapidly mixing Markov chains, analysis of boolean functions, expander graphs, and other gems in the design and analysis of probabilistic algorithms. Parts a & b are offered in alternate years.
Instructor: Schulman.

CS 151.
Complexity Theory.
12 units (3-0-9):
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.
Instructor: Umans.

CS 152.
Introduction to Cryptography.
12 units (3-0-9):
first term.
Prerequisites: Ma 1b, CS 21, CS 38 or equivalent recommended.
This course is an introduction to the foundations of cryptography. The first part of the course introduces fundamental constructions in private-key cryptography, including one-way functions, pseudo-random generators and authentication, and in public-key cryptography, including trapdoor one-way functions, collision-resistant hash functions and digital signatures. The second part of the course covers selected topics such as interactive protocols and zero knowledge, the learning with errors problem and homomorphic encryption, and quantum cryptography: quantum money, quantum key distribution. The course is mostly theoretical and requires mathematical maturity. There will be a small programming component. Not offered 2020-21.

CS/IDS 153.
Current Topics in Theoretical Computer Science.
9 units (3-0-6):
third 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 2020-21.

CMS/CS/CNS/EE/IDS 155.
Machine Learning & Data Mining.
12 units (3-3-6):
second term.
Prerequisites: CS/CNS/EE 156 a.
Having a sufficient background in algorithms, linear algebra, calculus, probability, and statistics, is highly recommended. This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. We will also discuss recent research developments.
Instructor: Pachter.

CS/CNS/EE 156 ab.
Learning Systems.
9 units (3-1-5):
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: Abu-Mostafa.

IDS/ACM/CS 157.
Statistical Inference.
9 units (3-2-4):
third term.
Prerequisites: ACM/EE/IDS 116, Ma 3.
Statistical Inference is a branch of mathematical engineering that studies ways of extracting reliable information from limited data for learning, prediction, and decision making in the presence of uncertainty. This is an introductory course on statistical inference. The main goals are: develop statistical thinking and intuitive feel for the subject; introduce the most fundamental ideas, concepts, and methods of statistical inference; and explain how and why they work, and when they don't. Topics covered include summarizing data, fundamentals of survey sampling, statistical functionals, jackknife, bootstrap, methods of moments and maximum likelihood, hypothesis testing, p-values, the Wald, Student's t-, permutation, and likelihood ratio tests, multiple testing, scatterplots, simple linear regression, ordinary least squares, interval estimation, prediction, graphical residual analysis.
Instructor: Zuev.

IDS/ACM/CS 158.
Fundamentals of Statistical Learning.
9 units (3-3-3):
third term.
Prerequisites: Ma 3 or ACM/EE/IDS 116, IDS/ACM/CS 157.
The main goal of the course is to provide an introduction to the central concepts and core methods of statistical learning, an interdisciplinary field at the intersection of statistics, machine learning, information and data sciences. The course focuses on the mathematics and statistics of methods developed for learning from data. Students will learn what methods for statistical learning exist, how and why they work (not just what tasks they solve and in what built-in functions they are implemented), and when they are expected to perform poorly. The course is oriented for upper level undergraduate students in IDS, ACM, and CS and graduate students from other disciplines who have sufficient background in probability and statistics. The course can be viewed as a statistical analog of CMS/CS/CNS/EE/IDS 155. Topics covered include supervised and unsupervised learning, regression and classification problems, linear regression, subset selection, shrinkage methods, logistic regression, linear discriminant analysis, resampling techniques, tree-based methods, support-vector machines, and clustering methods. Not offered 2020-21.

CS/CNS/EE/IDS 159.
Advanced Topics in Machine Learning.
9 units (3-0-6):
third term.
Prerequisites: CS 155; strong background in statistics, probability theory, algorithms, and linear algebra; background in optimization is a plus as well.
This course focuses on current topics in machine learning research. This is a paper reading course, and students are expected to understand material directly from research articles. Students are also expected to present in class, and to do a final project. Not offered 2020-21.

EE/CS/IDS 160.
Fundamentals of Information Transmission and Storage.
9 units (3-0-6):
second term.
Basics of information theory: entropy, mutual information, source and channel coding theorems. Basics of coding theory: error-correcting codes for information transmission and storage, block codes, algebraic codes, sparse graph codes. Basics of digital communications: sampling, quantization, digital modulation, matched filters, equalization.
Instructor: Kostina.

EE/CS 161.
Big Data Networks.
9 units (3-0-6):
third term.
Prerequisites: Linear Algebra ACM/IDS 104 and Introduction to Probability Models ACM/EE/IDS 116 or their equivalents.
Next generation networks will have tens of billions of nodes forming cyber-physical systems and the Internet of Things. A number of fundamental scientific and technological challenges must be overcome to deliver on this vision. This course will focus on (1) How to boost efficiency and reliability in large networks; the role of network coding, distributed storage, and distributed caching; (2) How to manage wireless access on a massive scale; modern random access and topology formation techniques; and (3) New vistas in big data networks, including distributed computing over networks and crowdsourcing. A selected subset of these problems, their mathematical underpinnings, state-of-the-art solutions, and challenges ahead will be covered. Given in alternate years. Not offered 2020-21.
Instructor: Hassibi.

CS/IDS 162.
Data, Algorithms and Society.
9 units (3-0-6):
second term.
Prerequisites: CS 38 and CS 155 or 156a.
This course examines algorithms and data practices in fields such as machine learning, privacy, and communication networks through a social lens. We will draw upon theory and practices from art, media, computer science and technology studies to critically analyze algorithms and their implementations within society. The course includes projects, lectures, readings, and discussions. Students will learn mathematical formalisms, critical thinking and creative problem solving to connect algorithms to their practical implementations within social, cultural, economic, legal and political contexts. Enrollment by application. Taught concurrently with VC 72 and can only be taken once, as VC 72 or CS/IDS 162.
Instructors: Mushkin, Ralph.

CS/CNS/EE/IDS 165.
Foundations of Machine Learning and Statistical Inference.
12 units (3-3-6):
second term.
Prerequisites: CMS/ACM/IDS 113, ACM/EE/IDS 116, CS 156 a, ACM/CS/IDS 157 or instructor's permission.
The course assumes students are comfortable with analysis, probability, statistics, and basic programming. This course will cover core concepts in machine learning and statistical inference. The ML concepts covered are spectral methods (matrices and tensors), non-convex optimization, probabilistic models, neural networks, representation theory, and generalization. In statistical inference, the topics covered are detection and estimation, sufficient statistics, Cramer-Rao bounds, Rao-Blackwell theory, variational inference, and multiple testing. In addition to covering the core concepts, the course encourages students to ask critical questions such as: How relevant is theory in the age of deep learning? What are the outstanding open problems? Assignments will include exploring failure modes of popular algorithms, in addition to traditional problem-solving type questions.
Instructor: Anandkumar.

CMS/CS/EE 166.
Computational Cameras.
12 units (3-3-6):
third term.
Prerequisites: ACM 104 or ACM 107 or equivalent.
Computational cameras overcome the limitations of traditional cameras, by moving part of the image formation process from hardware to software. In this course, we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics, and vision. At the start of the course, we will study modern image processing and image editing pipelines, including those encountered on DSLR cameras and mobile phones. Then we will study the physical and computational aspects of tasks such as coded photography, light-field imaging, astronomical imaging, medical imaging, and time-of-flight cameras. The course has a strong hands-on component, in the form of homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class. Example homework assignments include building an end-to-end HDR imaging pipeline, implementing Poisson image editing, refocusing a light-field image, and making your own lensless "scotch-tape" camera.
Instructor: Bouman.

EE/CS/IDS 167.
Introduction to Data Compression and Storage.
9 units (3-0-6):
third term.
Prerequisites: Ma 3 or ACM/EE/IDS 116.
The course will introduce the students to the basic principles and techniques of codes for data compression and storage. The students will master the basic algorithms used for lossless and lossy compression of digital and analog data and the major ideas behind coding for flash memories. Topics include the Huffman code, the arithmetic code, Lempel-Ziv dictionary techniques, scalar and vector quantizers, transform coding; codes for constrained storage systems. Given in alternate years; Not offered 2020-21.
Instructor: Kostina.

CS/CNS 171.
Computer Graphics Laboratory.
12 units (3-6-3):
first term.
Prerequisites: Extensive programming experience and proficiency in linear algebra, starting with CS 2 and Ma 1 b.
This is a challenging course that introduces the basic ideas behind computer graphics and some of its fundamental algorithms. Topics include graphics input and output, the graphics pipeline, sampling and image manipulation, three-dimensional transformations and interactive modeling, basics of physically based modeling and animation, simple shading models and their hardware implementation, and some of the fundamental algorithms of scientific visualization. Students will be required to perform significant implementations.
Instructor: Barr.

CS/CNS 174.
Computer Graphics Projects.
12 units (3-6-3):
third term.
Prerequisites: Extensive programming experience, CS/CNS 171 or instructor's permission.
This laboratory class offers students an opportunity for independent work including 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 possible improvements to the basic methods. May be repeated for credit with instructor's permission.
Instructor: Barr.

EE/CS/MedE 175.
Digital Circuits Analysis and Design with Complete VHDL and RTL Approach.
9 units (3-6-0):
third term.
Prerequisites: medium to advanced knowledge of digital electronics.
A careful balance between synthesis and analysis in the development of digital circuits plus a truly complete coverage of the VHDL language. The RTL (register transfer level) approach. Study of FPGA devices and comparison to ASIC alternatives. Tutorials of software and hardware tools employed in the course. VHDL infrastructure, including lexical elements, data types, operators, attributes, and complex data structures. Detailed review of combinational circuits followed by full VHDL coverage for combinational circuits plus recommended design practices. Detailed review of sequential circuits followed by full VHDL coverage for sequential circuits plus recommended design practices. Detailed review of state machines followed by full VHDL coverage and recommended design practices. Construction of VHDL libraries. Hierarchical design and practice on the hard task of project splitting. Automated simulation using VHDL testbenches. Designs are implemented in state-of-the-art FPGA boards. Not Offered 2020-21.
Instructor: Pedroni.

CS 176.
Computer Graphics Research.
9 units (3-3-3):
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. Not offered 2020-21.

CS/ACM 177 a.
Discrete Differential Geometry: Theory and Applications.
9 units (3-3-3):
second term.
Working knowledge of multivariate calculus and linear algebra as well as fluency in some implementation language is expected. Subject matter covered: differential geometry of curves and surfaces, classical exterior calculus, discrete exterior calculus, sampling and reconstruction of differential forms, low dimensional algebraic and computational topology, Morse theory, Noether's theorem, Helmholtz-Hodge decomposition, structure preserving time integration, connections and their curvatures on complex line bundles. Applications include elastica and rods, surface parameterization, conformal surface deformations, computation of geodesics, tangent vector field design, connections, discrete thin shells, fluids, electromagnetism, and elasticity.
Instructor: Desbrun.

CS/IDS 178.
Numerical Algorithms and their Implementation.
9 units (3-3-3):
third term.
Prerequisites: CS 2.
This course gives students the understanding necessary to choose and implement basic numerical algorithms as needed in everyday programming practice. Concepts include: sources of numerical error, stability, convergence, ill-conditioning, and efficiency. Algorithms covered include solution of linear systems (direct and iterative methods), orthogonalization, SVD, interpolation and approximation, numerical integration, solution of ODEs and PDEs, transform methods (Fourier, Wavelet), and low rank approximation such as multipole expansions.
Instructor: Desbrun.

CS 179.
GPU Programming.
9 units (3-3-3):
third term.
Prerequisites: Good working knowledge of C/C++.
Some experience with computer graphics algorithms preferred. 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 use nVidia's parallel computing architecture, CUDA. Labwork requires extensive programming.
Instructor: Barr.

CS 180.
Master’s Thesis Research.
Units (total of 45) are determined in accordance with work accomplished.:
.

Bi/BE/CS 183.
Introduction to Computational Biology and Bioinformatics.
9 units (3-0-6):
second term.
Prerequisites: Bi 8, CS 2, Ma 3; or BE/Bi 103 a; or instructor's permission.
Biology is becoming an increasingly data-intensive science. Many of the data challenges in the biological sciences are distinct from other scientific disciplines because of the complexity involved. This course will introduce key computational, probabilistic, and statistical methods that are common in computational biology and bioinformatics. We will integrate these theoretical aspects to discuss solutions to common challenges that reoccur throughout bioinformatics including algorithms and heuristics for tackling DNA sequence alignments, phylogenetic reconstructions, evolutionary analysis, and population and human genetics. We will discuss these topics in conjunction with common applications including the analysis of high throughput DNA sequencing data sets and analysis of gene expression from RNA-Seq data sets.
Instructors: Pachter, Thomson.

CNS/Bi/EE/CS/NB 186.
Vision: From Computational Theory to Neuronal Mechanisms.
12 units (4-4-4):
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; Not Offered 2020-21.
Instructors: Meister, Perona, Shimojo, Tsao.

CNS/Bi/Ph/CS/NB 187.
Neural Computation.
9 units (3-0-6):
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 rate-code neural networks, their differential equations, and equivalent circuits; stochastic models and their energy functions; associative memory; supervised and unsupervised learning; development; spike-based computing; single-cell computation; error and noise tolerance. Not Offered 2020-21.
Instructor: Perona.

BE/CS/CNS/Bi 191 ab.
Biomolecular Computation.
9 units (3-0-6) second term; (2-4-3) third term:
second, third terms.
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 self-assembly, 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.
Instructor: Winfree.

BE/CS 196 a.
Design and Construction of Programmable Molecular Systems.
12 units (3-6-3):
second term.
Prerequisites: none.
This course will introduce students to the conceptual frameworks and tools of computer science as applied to molecular engineering, as well as to the practical realities of synthesizing and testing their designs in the laboratory. In part a, students will design and construct DNA logic circuits, biomolecular neural networks, and self-assembled DNA nanostructures, as well as quantitatively analyze the designs and the experimental data. Students will learn laboratory techniques including fluorescence spectroscopy and atomic force microscopy, and will use software tools and program in MATLAB or Mathematica. Enrollment in part a is limited to 12 students. Offered 2020-2021.
Instructor: Qian.

Ph/CS 219 abc.
Quantum Computation.
9 units (3-0-6):
first, second terms.
Prerequisites: Ph 125 ab 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 error-correcting codes, quantum cryptography and teleportation. Overview of classical complexity theory, quantum complexity, efficient quantum algorithms, fault-tolerant quantum computation, physical implementations of quantum computation. Part c not offered in 2020-21.
Instructors: Preskill, Kitaev.

CS 274 abc.
Topics in Computer Graphics.
9 units (3-3-3):
first, second, third terms.
Prerequisites: instructor's permission.
Each term will focus on some topic in computer graphics, such as geometric modeling, rendering, animation, human-computer interaction, or mathematical foundations. The topics will vary from year to year. May be repeated for credit with instructor's permission. Not offered 2020-21.

CS 280.
Research in Computer Science.
Units in accordance with work accomplished:
.
Approval of student's research adviser and option adviser must be obtained before registering.

CS 282 abc.
Reading in Computer Science.
6 units or more by arrangement:
first, second, third terms.
Instructor's permission required.

CS 286 abc.
Seminar in Computer Science.
3, 6, or 9 units, at the instructor's discretion:
.
Instructor's permission required.

CS 287.
Center for the Mathematics of Information Seminar.
3, 6, or 9 units, at the instructor's discretion:
first, second, third terms.
Instructor's permission required.
Instructor: Staff.

### Please Note

The online version of the Caltech Catalog is provided as a convenience; however, the printed version is the only authoritative source of information about course offerings, option requirements, graduation requirements, and other important topics.