Graduate

Basic teaching techniques for teaching assistants, including responsibilities and rights of teaching assistants, resource materials, computer security, leading discussion or lab sessions, presentation techniques, maintaining class records, electronic handling of homework, and grading. The course examines research and professional training, including use of the library and online databases, technical typesetting, writing journal and conference papers, publishing in computer science and computer engineering, giving talks in seminars and conferences, and ethical issues in science and engineering. Required for all teaching assistants. Formerly CMPS 200 and CMPE 200.)

Rigorous analysis of the time and space requirements of important algorithms, including worst case, average case, and amortized analysis. Techniques include order-notation, recurrence relations, information-theoretic lower bounds, adversary arguments. Analysis of the key data structures: trees, hash tables, balanced tree schemes, priority queues, Fibonacci and binomial heaps. Algorithmic paradigms such as divide and conquer, dynamic programming, union-find with path compression, augmenting paths. Selected advanced algorithms. Introduction to NP-completeness. (Formerly Computer Science 201.)

Fundamental combinatorial algorithms, graph algorithms, flow problems, matching problems, linear programming, integer programming, NP-completeness, approximation algorithms for optimization problems. (Formerly Computer Science 211.)

Course on randomized algorithms, meant for graduate students with a good mathematical background. Students need familiarity with discrete math, analysis of algorithms, basic analysis, probability theory, and graph theory.

Finite automata and regular expressions, universal models of computation, computability and unsolvability, relations between complexity classes, hierarchy theorems, reductions, complete problems for the major complexity classes (L, NL, P, NP, PSPACE). Other topics may include complexity of counting and enumeration problems, complexity of approximation, randomized complexity classes. (Formerly Computer Science 210.)

The applications and uses of formal systems to computer science. Covers the syntax and semantics of propositional logic and first-order logic, normal forms, soundness and completeness theorems, Herbrand's theorem, unification and resolution, foundations of logic programming, automated theorem proving. Other topics may include deductive databases, database query languages, nonmonotonic reasoning. (Formerly Computer Science 217.)

Focuses on foundational aspects of modern cryptography, namely, private and public key cryptography; digital signatures; pseudorandom generators and functions/permutations; message authentication codes; hash functions; and random oracles, certificates and public-key infrastructures. Also connects the formal concepts with real-world applications. Students are exposed to general concepts of probability theory, number theory, and rigorous proofs. Assumes background on proofs, discrete probability, and modular arithmetic as well as programming in C/C++ or Python. Prerequisite(s): CSE 201 is recommended. Enrollment is restricted to graduate students and requires instructor permission.

Explores graph theory and algorithms for solving problems in engineering. A review of basic graph concepts and algorithms is followed by topics in network flow, partitioning, spectral analysis of graphs, graph isomorphism, and intractability. (Formerly Computer Engineering 277.)

Graduate course on the modern algorithmic toolbox, meant for graduate students with a good mathematical background. Students need familiarity with discrete math, analysis of algorithms, basic analysis, probability theory, and graph theory. Class is aimed at students who want to learn more about the central ideas and algorithmic techniques that are used in modern data science applications. Class focuses on the theoretical underpinnings of these algorithms as well as their practical applications and implementation details.

Covers current issues in programming languages. Language topics include object oriented, concurrent, functional, and logic programming, and other programmable applications such as symbolic manipulators and simulation. (Formerly Computer Science 203.)

Covers issues in the design, implementation, analysis, and specification of programming languages. Topics include formal semantics (including operational, axiomatic, and denotational semantics), advanced type systems, program analysis (including abstract interpretation and model checking), specification, and verification. (Formerly Computer Science 253.)

Advanced study of compiler implementation. Topics include compiler structure back end, run-time environments, storage management, garbage collection, register allocation, code generation, basic blocks, control flow, data flow, local and global optimization, interpretation, machine code generation. Students may not receive credit for this course and CSE 110B. Taught in conjunction with CSE 110B. (Formerly Computer Science 204.)

Introduction to the general principles of software engineering. Covers current and classical topics from both practical and theoretical viewpoints. Topics include software evolution, project management, software inspections, design methods, requirements analysis and specification, software testing, maintenance, software implementation, human interfaces, and software engineering experimentation. (Formerly CMPS 276.)

Detailed study of interlocking business, organizational, and technical issues in large-scale software reuse and component-based software engineering. Topics include architecture, design for reuse, domain engineering, model-driven development, domain-specific kits, components, frameworks, software agents, generators, problem-oriented languages, library design, reuse tools, patterns, and aspects. Assumes prior exposure to software engineering topics. (Formerly Computer Science 279.)

Advanced course on principles of database systems. Main topics include overview of the relational data model and relational query languages; recursive queries, datalog, and fixed-points; query processing and optimization; database design, dependencies, normal forms, and the chase procedure. Additional topics may include information integration, complex objects, semistructured data, and XML. (Formerly Computer Science 277.)

Advanced course in implementation techniques for database systems. For students who wish to do research in databases or to learn more about large-scale data processing. Topics include: indexing of complex data; techniques for high-volume concurrency control; query processing and optimization; database recovery; parallel database system architectures; database systems for streaming data; approximate query answering. Additional topics may include: self-managing database systems; advanced query optimization techniques; and query processing techniques for semi-structured data.

Mathematical techniques for analyzing systems to prove rigorous guarantees about their behavior. Fundamental algorithms for and advanced topics in modeling, specification, verification, correct-by-construction synthesis, and testing. Applications to hardware/software design, cybersecurity, robotics, machine learning. Course includes a final project.

Provides a thorough and fundamental treatment of the art of computer architecture. Topics include concepts of von Neumann architectures, methods of evaluating CPU performance, instruction-set design and examples, compiler issues, instruction pipelining, superscalar processors, methods for reduction of branch penalty, memory hierarchies, I/O systems, floating-point arithmetic, and current issues in parallel processing. (Formerly CMPE 202.)

Introduction to latest advances in computer architecture. Focuses on processor core design. Topics include simultaneous multithreading, thread level speculation, trace caches, novel out-of-order mechanisms, and energy-efficient processor core designs. Final project is modification/enhancement of an out-of-order processor on an FPGA development system. (Formerly Computer Engineering 221.)

Laboratory sequence illustrating topics covered in course 221. (Formerly Computer Engineering 221L.)

Advanced Very Large Scale Integrated (VLSI) custom integrated circuits. Topics include: semiconductors; field-effect transistors (FETs); circuits; and interconnect simulation, along with advanced material on manufacturability, variability, short-channel devices, and non-volatile memories. Students cannot receive credit for this course and CSE 122. (Formerly Computer Engineering 222.)

Design methodologies for Application Specific Integrated Circuits (ASICs). Topics include: behavioral specification; logic synthesis; standard-cell libraries; advanced timing analysis; and physical design automation tools. Familiarizes students with real-world tools during the design of a small system-on-a-chip project. Students are encouraged to fabricate and test their chips in an independent study. (Formerly Computer Engineering 223.)

Verilog digital logic design with emphasis on ASIC and FPGA design. Students design and verify large-scale systems. Assignments and project use the Verilog Hardware Description Language with emphasis on verification and high-frequency ASIC/FPGA targets. Course may be taught in conjunction with CSE 125.

Introduction to programming advanced parallel computer architecture. Topics may include: SIMD massively parallel processor arrays; streaming parallel coprocessors, such as graphics cards used for general-purpose processing (GPGPU); or other hybrid MIMD/SIMD architectures. Course has programming lab component, a project, and student presentation on related topics. (Formerly Computer Engineering 220.)

Agile Hardware Design techniques take the best of software engineering methods and apply them to improve hardware design productivity. Agile approaches not only reduce the time to solution, but they can also produce solutions which are better tailored for their target problems. In this synchronous in-person course, these techniques are covered while taking advantage of the Chisel hardware design language which brings the strengths of functional object-oriented programming to hardware design. Course consists of engaging lectures (intermixed coding demos and guest speakers) and progressive design assignments that culminate in a small project.

Design methods for Field-Programmable Gate Arrays (FGPAs), including algorithms for technology mapping, routability estimation, placement, and routing. The relationship between FPGA architectures and their computer-aided design tools. Course project involves the modification and analysis of an FPGA tool. (Formerly Computer Engineering 229.)

A detailed study of the issues involved in operating systems design and implementation. Readings cover current research topics and systems of historical significance. Topics include (but are not restricted to) process and memory management, protection, security, synchronization, performance evaluation, file systems, distributed systems. (Formerly Computer Science 221.)

Overview of research topics in distributed computer systems. Topics may include communication paradigms, process management, naming, synchronization and coordination, consistency and replication, fault tolerance, and security. Examples include distributed operating systems, distributed file and object systems, distributed document systems, and peer-to-peer systems. (Formerly Computer Science 232.)

Overview of research topics in computer and network security. Topics may include cryptographic operations, security properties and policies, authentication and access control, attacks on computer systems and defenses against them, security in programming languages, and network protocols for security. (Formerly CMPS 223.)

Cryptography has become ubiquitous, from light bulbs to atomic weapons. This course provides both a comprehensive introduction to applied cryptography and an additional focus on the human issues caused by bad implementations, bad processes, and broken algorithms. Knowledge of C Programming, Linux, and Virtual machines is required. (Formerly Computer Engineering 236 and Computer Science 236.)

Cyber-physical systems now permeate our lives; they include autonomous vehicles, the Internet of things, and modern control of our critical infrastructure such as the power grid. Learning about the threats against these systems and the possible defenses is essential for computer security practitioners. In this course, students read and analyze the latest published research in this area, and work on projects to address new problems.

Topics include storage devices, storage architectures, local file systems, high-performance file systems, and next-generation storage devices and architectures; covers issues of performance, reliability, scalability, robustness, and security. (Formerly Computer Science 229.)

Prepares students for doing research in artificial intelligence. Major topics covered are search and heuristics, knowledge representation, planning, deduction and inference, reinforcement learning, associative pattern retrieval, and adaptive search. Discussion includes current research issues in AI problem-solving methods. Individualized projects. (Formerly Computer Science 240.)

Introduction to the acquisition, representation, and application of knowledge in expert systems. Topics include production systems, backward and forward chaining, dependency-directed backtracking, reasoning with uncertainty, certainty factors, fuzzy systems, knowledge representation (rules, frames, and semantic nets), inference engines, and metaknowledge. Discussion includes current research issues in adaptive expert systems. Involves one major project. Undergraduates may enroll in this course if they have completed CSE 140. (Formerly Computer Science 241.)

Introduction to machine learning algorithms. Covers learning models from fields of statistical decision theory and pattern recognition, artificial intelligence, and theoretical computer science. Topics include classification learning and the Probably Approximately Correct (PAC) learning framework, density estimation and Bayesian learning, EM, regression, and online learning. Provides an introduction to standard learning methods such as neural networks, decision trees, boosting, nearest neighbor, and support vector machines. Requirements include one major experimental learning project or theoretical paper. Students may not receive credit for both this course and CSE 142. (Formerly CMPS 242.)

Covers the principles, algorithms, and applications of data mining, including mining sequential data, structured data, stream data, text data, spatiotemporal data, biomedical data, and other forms of complex data. Formerly TIM 245.)

Provides foundations of deep learning algorithms and principles. Topics include neural networks, deep learning principles, deep learning architectures such as convolutional neural networks and recurrent neural networks, autoencoders, generative adversarial networks, and reinforcement learning. (CSE 244A and CSE 244B formerly offered as one course, CSE 244.)

Introduction to machine learning models and algorithms for Natural Language Processing. Covers deep learning approaches and traditional machine learning models. Topics include an introduction to standard neural network learning methods such as feed-forward neural networks, recurrent neural networks, convolutional neural networks, and encoder-decoder models with applications to natural language processing problems such as utterance classification and sequence tagging. Requirements include a midterm, final, programming assignments, and a project. (CSE 244A and CSE 244B formerly offered as one course, CSE 244.)

Focuses on classic and current theories and research topics in the computational modeling of discourse and dialogue, with applications to human-computer dialogue interactions; dialogue interaction in computer games and interactive story systems; and processing of human-to-human conversational and dialogue-like language such as e-mails. Topics vary depending on the current research of the instructor(s) and the interests of the students. Students read theoretical and technical papers from journals and conference proceedings and present class lectures. A research project is required.

Graduate course covering basics of data science literacy and data science ethics. Topics include algorithmic discrimination, fairness, interpretability, privacy, and reproducibility. Key statistical topics such as generalization, causality, curse of dimensionality, and sampling bias are covered.

Overview of artificial intelligence (AI) and machine learning (ML) and principles, implementation and deployment pipeline, and approaches in solving domain-related problems. Topics covered through direct instruction, invited guest speakers, reviews of state-of-art research papers, and a team project. Students are given an opportunity to work on a quarter-long AI/ML project to be counted toward their master's degree project requirements. Enrollment is by instructor consent. Prior experience in machine learning and deep learning is required. (Formerly AI: Problem Solving and Intelligent Search.)

Examines the mathematical and algorithmic foundations of data science including high dimensional data, probabilistic inequalities, dimensionality reduction, correlation detection, streaming algorithms, and clustering. (Formerly Computer Science 218.)

Provides a systematic methodology and corresponding set of methods and analytical tools in stochastic models; reinforcement learning; stochastic (neuro-)dynamic programming; Bayesian graphical models; inference; and social networks used for web analytics and machine learning to achieve business intelligence (BI) and support research and applications in computer science, computer engineering, and electrical engineering, applied mathematics and statistics, business, management, and economics. Includes exposure to Hadoop for large-scale computation. Students should have solid background in probability equivalent to statistics, stochastic, methods, calculus, (and preferably) stochastic processes and optimization, or mathematical maturity and exposure to business intelligence and algorithms. (Formerly TIM 251.)

Issues resulting from organizing communication among autonomous computers. Includes network models and switching techniques; medium access control protocols and local area networks; error control and retransmission strategies; routing algorithms and protocols; congestion control mechanisms and end-to-end protocols; application-level protocols; and application of concepts to wireless and wireline networks, with emphasis on the Internet. (Formerly Computer Engineering 252A.)

Focuses on the design and analysis of protocols for computer communication. Topics include: the safety, liveliness, and performance of communication protocols for medium access control (MAC); link control; routing and switching; multicasting; and end-to-end transport. Students cannot receive credit for this course and CSE 152. (Formerly CMPE 252B.)

Fiber-optic technology; fiber-optic link design; network protocol concepts; coding and error control; high-speed local area and metropolitan area networks; gigabit networks; error and congestion control; photonic networks; research topics. (Formerly Computer Engineering 254.)

Fundamental mechanisms for network security and their application in widely deployed protocols. In-depth treatment of security mechanism at the data-link, network, and transport layers for both wired and wireless networks. Covers mechanisms for privacy and integrity, and methods for intrusion detection. (Formerly CMPE 253.)

An interdisciplinary course on wireless communication and mobile computing. Covers the physical aspects of wireless communication but emphasizes higher protocol layers. Topics include cellular networks, packet radio and ad hoc networks, wireless transport protocols, security, and application-level issues. (Formerly Computer Engineering 257.)

Focuses on the networking aspects of sensor networks: protocols at the various layers and how they answer the specific requirements posed by these networks (e.g., data driven, energy efficient, etc.) and their applications (monitoring, tracking, etc.). Explores how physical layer and hardware issues may influence protocol design. (Formerly Computer Engineering 259.)

Introduces current research and techniques of modeling, 2D/3D transformation, matrix composition, shading algorithms, and rendering to obtain computer-generated imagery. Programming assignments and major project required. Students cannot receive credit for both this course and CSE 160. (Formerly Computer Science 260.)

Covers advanced topics in visualization, e.g., tensor-field visualization, uncertainty visualization, information visualization. Topics vary with differing offerings of the course. Course includes lectures, exam, research paper reading/presentation, and projects. Final project is expected to be at a sufficiently advanced level for submission to a conference. Students work individually or in pairs. (Formerly Computer Science 261.)

An in-depth treatment of computer animation, including its origins in conventional animation, 2-D animation, inbetweening, motion control, morphing, graphical motion editors, animation languages, motion blur, simulation of articulated body motion, real-time animation, and special-purpose animation hardware. (Formerly Computer Science 262.)

Explores high-quality interdisciplinary research using socio-economic data and software available on the Internet, and data curation, computation, and visualization to strengthen scientific inquiry to bear on large-scale societal problems. Applications include inequality, poverty, water, energy, environment, health, education, and democracy. Enrollment restricted to graduate students. Enrollment by instructor consent. (Formerly Computer Science 263.)

Introduces general concepts in computer vision, with an emphasis on geometric 3D reconstruction. Topics include radiometry, photometry, projective geometry, geometric camera model, epipolar geometry, stereo depth reconstruction, corner and edge features, point descriptors and matching, and optical flow. (Formerly Computer Engineering 264.)

Theory and hands-on practice to understand what makes user interfaces usable and accessible to diverse individuals. Covers human senses and memory and their design implications, requirement solicitation, user-centered design and prototyping techniques, and expert and user evaluations. Individual research project. Interdisciplinary course for art, social science and engineering graduate students. Students cannot receive credit for this course and CSE 165.

Course covers major topics of information retrieval including: characteristics and representation of text, several important retrieval and ranking models, content recommendation and classification; distributed or federated search, AI semantics and dialog for information access; human factors and interfaces; and evaluation, and domain-specific applications. A research project is required. (Formerly TIM 260.)

A first graduate course in optimization with an emphasis on problems arising in management and engineering applications. Objectives are to become experts in problem formulation, comfortable with software for solving these problems, and familiar with analytical methods behind these solver technologies. (Formerly TIM 206.)

A first graduate course in stochastic process modeling and analysis with an emphasis on applications in technology management, information systems design, and engineering. (Formerly TIM 207.)

Provides students with systematic methodology and analytical tools in data and text mining and business analytics. Also provides an integrated perspective and examines use of these methods in the field of knowledge services, such as online marketing, sponsored search, health care, financial services, recommender systems, etc. Includes training in the basic elements of stochastic optimization and other algorithmic approaches, such as stochastic dynamic programming, statistics, constrained optimization, and machine learning with exposure to software tools. These methods enable firms to achieve rapid, effective, and profitable optimization of knowledge-services management. (Formerly TIM 209.)

Weekly seminar covering topics of current research in computer science. Enrollment by permission of instructor. (Formerly CMPS 280A, Seminar in Computer Science Research.)

Covers advanced research topics from the recent literature in database systems and related fields. Involves presentations from UCSC students and faculty, and guest talks from researchers in industry and other academic institutions. Enrollment by permission of instructor. (Formerly 280D.)

Weekly seminar covering topics of current research in software engineering. (Formerly CMPS 280G.)

Weekly seminar on advanced topics in VLSI and computer-aided design (CAD). Students present and discuss modern issues in semiconductor design, fabrication, and CAD. Frequent guest speakers present pertinent results from industry and academia. (Formerly Computer Engineering 280G.)

Covers advanced topics and current research in the general area of human computation. Material is drawn from several disciplines that involve or deal with human computation, including computer vision, human-computer interaction, databases, and machine learning. The course comprises presentations from faculty, enrolled students, and external visitors. (Formerly CMPS 280H.)

Seminar series discussing advanced topics in information retrieval and knowledge management. Current research and literature are presented during each meeting. (Formerly 280I.)

Weekly seminar covering topics of current research in computer graphics. Enrollment restricted to graduate students and by permission of instructor. (Formerly CMPS 280J.)

Perspective on the theory, plus examples, and tools useful to technologists and engineers for successfully guiding and supporting sales and marketing endeavors and, thereby, ensuring funding, staffing, product appeal, positive customer relationships, and marketplace success. (Formerly TIM 280M.)

Covers advanced research topics from the recent literature in the uses of logic in computer science with particular emphasis on the applications of logic to the representation and the management of data. Involves presentations from UCSC students and faculty, and guest talks from researchers in other academic institutions or industrial research labs. Enrollment is by permission of the instructor and is restricted to graduate students. (Formerly CMPS 280L.)

Weekly seminar covering topics of current interest in machine learning. Enrollment is by permission of the instructor. Enrollment is restricted to graduate students. (Formerly CMPS 280M.)

Weekly seminar series covering topics of current research in networks and networked systems. Current research work and literature in these areas are discussed. Prerequisite(s): permission of instructor. Enrollment is restricted to graduate students. (Formerly Computer Engineering 280N.)

Covers current research in language-based approaches to security, distributed systems, databases, and formal verification. Students read and present papers from academic journals and conferences.

Weekly seminar series covering topics of current research in parallel systems, architectures, and algorithms. Current research work and literature in these areas are discussed. (Formerly Computer Engineering 280P.)

Weekly seminar series covering topics of current research in computer systems. Enrollment by permission of instructor. (Formerly CMPS 280S AND CMPE 280S.)

Weekly seminar series in which distinguished speakers from industry, universities, and government discuss current developments in networking and computer technology. The emphasis is on open research questions that may lead to collaborative work with faculty and graduate students. (Formerly Computer Engineering 280T.)

Weekly graduate-level seminar series discussing advanced topics in computer vision and image analysis. Current research and literature presented during each meeting. Enrollment is by permission of the instructor. Enrollment is restricted to graduate students. (Formerly Computer Engineering 280V.)

Covers advanced research topics from the recent literature in distributed systems and related fields. Involves presentations from UCSC students and faculty. Enrollment is restricted to graduate students and by permission of the instructor.

Covers advanced topics and current research in natural language processing. Focuses on student presentations and seminar participation. Enrollment is restricted to graduate students. Enrollment is by permission of the instructor. (Formerly CMPS 280Z.)

Writing skills development for graduate engineers. Students produce a major writing project with many subtasks. Exercises includes fellowship application; mathematical and algorithmic description; use of tables and graphs; experiment description; and producing technical web sites, presentations, and posters. Enrollment is restricted to graduate students in biomolecular engineering, computer science and engineering, computer science, and electrical and computer engineering and by permission of the instructor. (Formerly CMPE 285.)

Graduate seminar in algorithms and complexity theory on topics from recently published research journal articles and conference proceedings. Topics vary from year to year depending on the current research of the instructor(s) and interests of students. Students read technical papers from relevant journals and conference proceedings and present class lectures. Guest lectures may supplement the student presentations. A research project and/or paper may be required. (Formerly Computer Science 290A.)

In-depth study of current research topics in machine learning. Topics vary from year to year but include multi-class learning with boosting and SUM algorithms, belief nets, independent component analysis, MCMC sampling, and advanced clustering methods. Students read and present research papers; theoretical homework in addition to a research project. (Formerly Computer Science 290C.)

An introduction to the design and analysis of neural network algorithms. Concentrates on large artificial neural networks and their applications in pattern recognition, signal processing, and forecasting and control. Topics include Hopfield and Boltzmann machines, perceptions, multilayer feed forward nets, and multilayer recurrent networks. (Formerly Computer Science 290D.)

Object-oriented programming methodology is the application of abstract-data types and polymorphism to coding solution. Topics geared to beginning thesis research in this field. (Formerly Computer Science 290E.)

Combinatorial mathematics, including summation methods, working with binomial coefficients, combinatorial sequences (Fibonacci, Stirling, Eulerian, Harmonic, Bernoulli numbers), generating functions and their uses, Bernoulli processes, and other topics in discrete probability. Oriented toward problem solving, applications mainly to computer science, but also physics. (Formerly Computer Science 290F.)

Research seminar on current topics in software engineering. Topics vary from year to year depending on the current research of the instructor(s) and interests of students. Students read technical papers from relevant journals and conference proceedings. Synthesis and understanding of materials is demonstrated by a required research project. (Formerly Computer Science 290G.)

Focuses on current research topics in database systems. Different offerings cover different topics depending on current research of instructor(s) and the interests of students. Students read technical papers from journals and conference proceedings and present class lectures. A research project is required. (Formerly Computer Science 290H.)

A graduate seminar in computer graphics on topics from recently published research journal articles and conference proceedings. Topics vary from year to year depending on interests of students. Primary areas of interest are likely to be scientific visualization, modeling, rendering, scattered data techniques, wavelets, and color and vision models. Students read technical papers and present class lectures. Guest lecturers supplement the student presentations. A research project is required. (Formerly Computer Science 290B.)

Teaches participants about current methods and directions in active areas of Natural Language Processing research and applications. Students perform independent research and hone skills with state-of-the-art NLP tools and techniques.

Explores the foundations of crowdsourcing and computer-mediated collaboration. Covers the algorithmic and statistical foundations of crowdsourcing, introducing and analyzing algorithms, and experimenting with concrete systems. Also, provides an introduction to computational systems for mediating user interaction and collaboration. (Formerly Computer Science 290L.)

Investigates selected topics in applied parallel computation. Topics may include numerical methods, artificial intelligence and machine learning algorithms, graphics and image processing, systolic algorithms, and the interplay between hardware and algorithms. Students are encouraged to investigate and discuss the parallelization of their own research. (Formerly Computer Engineering 290M.)

Selected topics of current interest in the area of computer system performance. Subjects may include aspects of large systems, performability, computer networks, storage subsystems, and nontraditional approaches and are subject to periodic revision. (Formerly Computer Engineering 290N.)

Focuses on some of the foundational aspects of convex and its relationship to modern machine learning. Discusses positive results--how can you solve convex optimization problems--and negative ones with statements like This family of problems is too hard to be solved in reasonable time. Course is divided into three parts, each exploring a different aspect of convex optimization: 1) algorithmic frameworks; 2) Oracle complexities; 3) the power of randomness. Through this course students are exposed to general concepts of convex geometry, learning theory, and rigorous proofs. (formerly CMPS 290O.)

Helps students achieve both expository knowledge and expertise in the field of data privacy. Focuses on fundamental techniques used in designing privacy-preserving, machine-learning systems in both academia and in the industry. Students are expected to read and understand recent research papers in the topic. (Formerly Computer Science 290P.)

Current research topics on computer programming languages. Topics vary year to year. Students read papers from current conferences and journals, and present class lectures. A research project is required. (Formerly Computer Science 290Q.)

Focuses on current research topics in computer systems. Topics vary from year to year depending on the current research of the instructor(s) and the interests of the students. Students read technical papers from current journals and conference proceedings, and present class lectures. A research project is required. (Formerly Computer Science 290S.)

Current research topics on computer technology that is intentionally targeted to benefiting society. Topics vary year to year. Students read papers from current conferences and journals, and present class lectures. A research project is required. (Formerly Computer Science 290T.)

Research seminar on encryption and related technologies. Topics include theory of codes, random sequences and generators, public key cryptosystems, private key cyphers, key exchange protocols, quantum computing and cryptography. Major project required. Prerequisite: interview with instructor. (Formerly Computer Science 290X.)

A graduate seminar on a research topic in computer engineering which varies according to instructor. Possible topics include, but are not limited to, communication networks, data compression, special-purpose architectures, computer arithmetic, software reliability and reusability, systolic arrays. (Formerly Computer Engineering 293.)

Independent completion of a masters project under faculty supervision. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although this course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although this course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although this course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although this course may be repeated for credit, not every degree program will accept a repeated course toward degree requirements.

Thesis research conducted under faculty supervision. Although the course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Although the course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Although the course may be repeated for credit, not every degree program will accept a repeated course towards degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Enrollment is restricted to graduate students. Recommended for part-time students. Students submit petition to sponsoring agency.