Course Description: Comprehensive introduction to artificial intelligence (AI) and its multifaceted applications. Foundational understanding of modern AI to enable effective communication about its functions, recognition of its applications, and awareness of its core principles. Ethical and societal implications of AI.

This version has ended; see updated course, below.
Course Description: Introduction to key computational ideas necessary to understand and produce digital media. Fundamentals of programming are covered as well as analysis of how media are represented and transmitted in digital form. Aimed primarily at non-computer science students.

Course Description: Display, processing, and representation of information and data on a computer. Understanding and analyzing the digital representations of numbers, images, and sounds. Basic computer operations and their logic. Introduction to discrete mathematics in computer science, including propositional logic, proofs by induction, recursions, and counting. Introduction to algorithms. Uses of computers and their influence on society.

Prerequisite(s): MAT 016A (can be concurrent) or MAT 017A (can be concurrent) or MAT 019A { can be concurrent } or MAT 021A (can be concurrent).

Course Description: Discrete mathematics of particular utility to computer science. Proofs by induction. Propositional and first-order logic. Sets, functions, and relations. Big-O and related notations. Recursion and solutions of recurrence relations. Combinatorics. Probability on finite probability spaces. Graph theory.

Prerequisite(s): MAT 016A C- or better or MAT 017A C- or better or MAT 019A C- or better or MAT 021A C- or better.

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Course Description: Introduction to programming and problem solving in Python. Aimed primarily at non-major students.

Course Description: Introduction to programming and problem solving in Python. Aimed primarily at non-major students.

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Course Description: Design and analysis of data structures using Python; trees, heaps, searching, sorting, and graphs.

Prerequisite(s): ECS 010 C- or better or ECS 030 C- or better or ECS 032A C- or better or ECS 036A C- or better.

Course Description: Programming in the C language. Use of basic UNIX tools. Writing good programs of increased complexity and efficiency. Implementation of data structures in C.

Prerequisite(s): ECS 032B C- or better.

Course Description: UNIX Operating system tools and programming environment. Methods for debugging and verification. Principles object-oriented programming in C++.

Prerequisite(s): ECS 032C C- or better; or consent of instructor.

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Course Description: Computers and computer programming for students with some prior experience, algorithm design, and debugging. Good programming style. Use of basic UNIX tools.

Prerequisite(s): ECS 032A C- or better or ECS 010 C- or better; or must satisfy computer science placement exam; prior experience with basic programming concepts (variable, loops, conditional statements) required.

Course Description: Object-oriented programming in C++. Basic data structures and their use. Writing good programs of increased complexity and efficiency. Methods for debugging and verification.

Prerequisite(s): ECS 030 C- or better or ECS 036A C- or better.

Course Description: Design and analysis of data structures for a variety of applications; trees, heaps, searching, sorting, hashing, and graphs. Extensive programming.

Prerequisite(s): (ECS 040 C- or better or ECS 036B C- or better); ECS 020 C- or better.

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Course Description: Comparative study of different hardware architectures via programming in the assembly languages of various machines. Role of system software in producing an abstract machine. Introduction to I/O devices and programming.

Prerequisite(s): ECS 040 C- or better or ECS 034 C- or better or ECS 036B C- or better.

Course Description: Foundations of normative ethics for technology, its relationships to moral philosophy and to the law. Technologies and their impacts on the environment and society. Justice, meritocracy, and computer science and engineering. Ethical issues with AI and machine learning.

Course Description: Special topics in Computer Science Theory.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Architecture.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Programming Languages & Compilers.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Operating Systems.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Software Engineering.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Databases.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Artificial Intelligence.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Computer Graphics.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Networks.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Computer-Aided Design.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Scientific Computing.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Computer Science.

Prerequisite(s): Consent of instructor.

Course Description: Supervised work experience in computer science.

Prerequisite(s): Lower division standing; project approval prior to period of internship.

Course Description: Directed group study.

Course Description: Student facilitated course intended primarily for lower division students.

Prerequisite(s): Consent of instructor.

Course Description: Special study for lower division students.

Prerequisite(s): Consent of instructor.

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Course Description: Machine learning methods and their application. Theory, design and evaluation of supervised/unsupervised machine learning algorithms. Practical use of matching learning methods and their challenges.

Prerequisite(s): (ECS 032B or ECS 036C); (MAT 135A or STA 035C).

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Course Description: Principles, mechanisms, implementation, and sound practices of computer security and data protection. Cryptography, authentication and access control. Internet security. Malicious software. Common vulnerabilities. Practical security in everyday life. Not intended for Computer Science or Computer Science & Engineering majors.

Prerequisite(s): ECS 010 or ECS 030 or ECS 032A or ECS 036A.

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Course Description: Overview of computer networks, World Wide Web, email, local & wide-area computer networks, TCP/IP protocol suite, network protocols for data transmission, introduction to network programming.

Prerequisite(s): ECS 032B.

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Course Description: Overview of Database Systems, Conceptual Modeling and Design, E/R diagrams, Relational Model, Relational Algebra, SQL, File and Index Structures, Query Evaluation, Transaction Concepts, Concurrency and Recovery, and NoSQL Databases.

Prerequisite(s): ECS 032B.

Course Description: Algorithms for searching, pattern matching, combinatorial problems, clustering, and time series analysis with practical emphasis.

Prerequisite(s): ECS 017 or ECS 020; ECS 032B or ECS 036C.

Course Description: Introduction to software systems for processing large datasets. Hands-on experience with scripting, data streams, distributed computing, and software development and deployment infrastructure.

Prerequisite(s): ECS 116 or ECS 165A.

Course Description: Fundamental ideas in the theory of computation, including formal languages, computability and complexity. Reducibility among computational problems.

Prerequisite(s): (ECS 020 or MAT 108); (ECS 32B or ECS 36C recommended).

Course Description: Complexity of algorithms, bounds on complexity, analysis methods. Searching, sorting, pattern matching, graph algorithms. Algorithm design techniques: divide-conquer, greedy, dynamic programming. Approximation methods. NP-complete problems.

Prerequisite(s): ECS 020; (ECS 060 or ECS 032B or ECS 036C).

Course Description: Theory and practice of hard problems, and problems with complex algorithm solutions. NP-completeness, approximation algorithms, randomized algorithms, dynamic programming and branch and bound. Theoretical analysis, implementation and practical evaluations. Examples from parallel, string, graph, and geometric algorithms.

Prerequisite(s): ECS 122A; (ECS 060 or ECS 034 or ECS 036C).

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Course Description: Fundamental biological, mathematical and algorithmic models underlying bioinformatics and systems biology; sequence analysis, database search, genome annotation, clustering and classification, functional gene networks, regulatory network inference, phylogenetic trees, applications of common bioinformatics tools in molecular biology and genetics.

Prerequisite(s): (ECS 032A or ECS 036A or ENG 006); (STA 032 or STA 035B or STA 100 or STA 131A or ECS 132 or MAT 135A or EEC 161 or BIM 105); (BIS 002A or MCB 010).

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Course Description: Introduction to the theory and practice of cryptographic techniques used in computer security. Encryption (secret-key and public-key), message authentication, digital signatures, entity authentication, key distribution, and other cryptographic protocols. The social context of cryptography.

Prerequisite(s): (ECS 020 or MAT 108); (ECS 010 or ECS 032A or ECS 030 or ECS 036A).

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Course Description: Fundamental biological, chemical and algorithmic models underlying computational structural biology; protein structure and nucleic acids structure; comparison of protein structures; protein structure prediction; molecular simulations; databases and online services in computational structural biology.

Prerequisite(s): (BIS 002A or MCB 010); (ECS 010 or ECS 032A or ECS 036A).

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Course Description: Matrix-vector approach using MATLAB for floating-point arithmetic, error analysis, data interpolation, least squares data fitting, quadrature, zeros, optimization and matrix eigenvalues and singular values. Parallel computing for matrix operations and essential matrix factorizations.

Prerequisite(s): (ECS 030 or ENG 006 or ECS 032A or ECS 010 or ECS 036A); (MAT 022A or MAT 027A or MAT 067).

Course Description: Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics.

Prerequisite(s): (ECS 040 or ECS 034 or ECS 036B); ECS 020; MAT 021C; (MAT 022A or MAT 027A or MAT 067).

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Course Description: Syntactic definition of programming languages. Introduction to programming language features including variables, data types, data abstraction, object-orientedness, scoping, parameter disciplines, exception handling. Non-imperative programming languages. Comparative study of several high-level programming languages.

Prerequisite(s): ECS 050; ECS 020; (ECS 034 or 036C); ECS 150 is recommended.

Course Description: Continuation of programming language principles. Further study of programming language paradigms such as functional and logic; additional programming language paradigms such as concurrent (parallel); key implementation issues for those paradigms; and programming language semantics.

Prerequisite(s): ECS 140A.

Course Description: Principles and techniques of lexical analysis, parsing, semantic analysis, code generation, and code optimization. Implementation of compilers.

Prerequisite(s): ECS 140A; ECS 120; ECS 122A recommended.

Course Description: Goals and philosophy of scripting languages, with Python and R as prime examples. Applications include networking, data analysis and display, and graphical user interfaces (GUIs).

Prerequisite(s): ECS 034 or ECS 036C or ECS 060; or consent of instructor.

Course Description: Basic concepts of operating systems and system programming. Processes and interprocess communication/synchronization; virtual memory, program loading and linking; file and I/O subsystems; utility programs. Study of a real operating system.

Prerequisite(s): (ECS 034 or ECS 036C or ECS 060); (ECS 154A or EEC 170).

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Course Description: Overview of computer networks, TCP/IP protocol suite, computer-networking applications and protocols, transport-layer protocols, network architectures, Internet Protocol (IP), routing, link-layer protocols, local area and wireless networks, medium access control, physical aspects of data transmission, and network-performance analysis.

Prerequisite(s): (ECS 060 or ECS 032B or ECS 036C); (ECS 132 or EEC 161 or MAT 135A or STA 131A or STA 120 or STA 032).

Course Description: TCP/IP protocol suite, computer networking applications, client-server and peer-to-peer architectures, application-layer protocols, transport-layer protocols, transport-layer interfaces, sockets, network programming, remote procedure calls, and network management.

Prerequisite(s): ECS 150; (ECS 152A or EEC 173A).

Course Description: Advanced topics in computer networks, wireless networks, multimedia networking, traffic analysis and modeling, network design and management, network simulation and performance analysis, and design projects in communication networks.

Prerequisite(s): EEC 173A or ECS 152A.

Course Description: Principles, mechanisms, and implementation of computer security and data protection. Policy, encryption and authentication, access control, and integrity models and mechanisms; network security; secure systems; programming and vulnerabilities analysis. Study of an existing operating system.

Prerequisite(s): ECS 150; (ECS 152A or EEC 173A).

Course Description: Introduction to digital design. Interfacing of devices for I/O, memory and memory management. Input/output programming, via wait loops, hardware interrupts and calls to operating system services. Hardware support for operating systems software.

Prerequisite(s): ECS 050 or EEC 070.

Course Description: Hardwired and microprogrammed CPU design. Memory hierarchies. Uniprocessor performance analysis under varying program mixes. Introduction to pipelining and multiprocessors.

Prerequisite(s): ECS 154A or EEC 170 or EEC 180A.

Course Description: Techniques for software development using the shared-memory and message-passing paradigms, on parallel architectures and networks of workstations. Locks, barriers, and other techniques for synchronization. Introduction to parallel algorithms.

Prerequisite(s): ECS 150; ECS 154B recommended.

Course Description: Requirements, specification, design, implementation, testing, and verification of large software systems. Study and use of software engineering methodologies.

Prerequisite(s): ECS 140A; extensive programming experience recommended.

Course Description: Concepts and practice of collaborative software development using modern software tools.

Prerequisite(s): (ECS 040 or ECS 032B or ECS 036B).

Course Description: Technical aspects of building websites, including both server-side and client-side software development.

Prerequisite(s): ECS 030 or ECS 034 or ECS 036B; or equivalent programming experience in C and the Unix environment.

Course Description: Art and science of information visualization and interfaces for information systems. Design principles of human-computer interaction. Visual display and navigation of nonspatial and higher dimensional data. Implementations, performance issues, tradeoffs, and evaluation of interactive information systems.

Prerequisite(s): ECS 060 or ECS 032B or ECS 036C.

Course Description: Introduction to concepts and practice of modern human-computer interaction design.

Course Description: Database modeling and design (E/R model, relational model), relational algebra, query languages (SQL), file and index structures, query processing, transaction management.

Prerequisite(s): ECS 060 or ECS 032B or ECS 036C.

Course Description: Data modeling (object-relational, graph-based, spatiotemporal models). Querying semistructured data (XML). Database theory (normalization, integration, provenance). Database programming (stored procedures, embedded SQL, web programming). Advanced topics (data warehousing, parallel data processing).

Prerequisite(s): ECS 165A; (ECS 060 or ECS 034 or ECS 036C).

Course Description: Design and implementation of intelligent computer systems. Knowledge representation and organization. Memory and inference. Problem solving. Natural language processing.

Prerequisite(s): ECS 060 or ECS 032B or ECS 036C.

Course Description: Introduction to machine learning. Supervised & unsupervised learning, including classification, dimensionality reduction, regression & clustering using modern machine learning methods. Applications of machine learning to other fields.

Prerequisite(s): (ECS 032B or ECS 036C); (STA 032 or STA 035B or STA 100 or STA 131A or MAT 135A or EEC 161 or ECS 132); (MAT 022A or MAT 027A or MAT 067).

Course Description: Collaborative filtering and content-based methods for building recommender systems. Statistical, matrix factorization, textual analysis, and nearest-neighbor approaches. Case studies.

Prerequisite(s): (ECS 032B or ECS 036B or ECS 040); (ECS 132 or STA 130A or STA 131A or ECN 140); (MAT 022A or MAT 027A or MAT 067).

Course Description: Techniques for automated extraction of high-level information from images generated by cameras, three-dimensional surface sensors, and medical devices. Typical applications include detection of objects in various types of images and describing populations of biological specimens appearing in medical imagery.

Prerequisite(s): (MAT 067 C- or better or MAT 027A C- or better or MAT 022A C- or better); (ECS 060 or ECS 032B or ECS 036C).

Course Description: Computer vision is the study of enabling machines to "see" the visual world; e.g., understand images and videos. Explores several fundamental topics in the area, including feature detection, grouping and segmentation, and recognition.

Prerequisite(s): (ECS 060 or ECS 032B or ECS 036C); recommended (STA 032 or STA 131A or MAT 135A or EEC 161 or ECS 132); (MAT 022A or MAT 27A or MAT 067).

Course Description: Principles of computer graphics, with a focus on interactive systems. Current graphics hardware, elementary operations in two-and three-dimensional space, geometric transformations, camera models and interaction, graphics system design, standard graphics APIs, individual projects.

Prerequisite(s): (ECS 060 or ECS 034 or ECS 036C); (MAT 022A or MAT 027A or MAT 067).

Course Description: Computer graphics techniques for generating images of various types of measured or computer-simulated data. Typical applications for these graphics techniques include study of air flows around car bodies, medical data, and molecular structures.

Prerequisite(s): ECS 175.

Course Description: Interactive graphics techniques for defining and manipulating geometrical shapes used in computer animation, car body design, aircraft design, and architectural design.

Prerequisite(s): ECS 175.

Course Description: Developing gameplay systems in the context of game design and software engineering. Aspects of technical game development that depend on the genre or details of a specific game design, thus making them difficult to abstract and engineer into game engines.

Prerequisite(s): ECS 032B or ECS 036C; extensive programming experience recommended.

Course Description: Foundations of ethics. Views of technology. Technology and human values. Costs and benefits of technology. Character of technological change. Social context of work in computer science and engineering.

Prerequisite(s): Upper division standing.

Course Description: Special topic in Computer Science Theory.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Architecture.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Programming Languages & Compilers.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Operating Systems.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in software engineering.

Prerequisite(s): Consent of instructor.

Course Description: Special topic Databases.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Artificial Intelligence.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer Graphics.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Networks.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer-Aided Design.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Scientific Computing.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer Science.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Computer Security.

Prerequisite(s): Consent of instructor.

Course Description: Special topics in Bioinformatics & Computational Biology.

Prerequisite(s): Consent of instructor.

Course Description: Research group conferences.

Prerequisite(s): Consent of instructor. Upper division standing in Computer Science or Computer Science and Engineering.

Course Description: Examination of a special topic in a small group setting.

Prerequisite(s): Senior standing.

Course Description: Student teams plan, implement, and evaluate large-scale projects involving computer and computational systems. Project proposed and supervised by a faculty member.

Prerequisite(s): ECS 160.

Course Description: Supervised work experience in computer science.

Prerequisite(s): Completion of a minimum of 84 units; project approval prior to period of internship.

Course Description: Responding to real-life client design challenges, student teams plan, implement, and evaluate large-scale projects involving computer and computational systems. Project supervised by a faculty member. Must take ECS 193A & ECS 193B to receive credit.

Prerequisite(s): ECS 160 (can be concurrent); ECS 150; upper division standing in Computer Science or Computer Science Engineering; or consent of instructor.

Course Description: Responding to real-life client design challenges, student teams plan, implement, and evaluate large-scale projects involving computer and computational systems. Project supervised by a faculty member. Must take ECS 193A & ECS 193B to receive credit.

Prerequisite(s): ECS 193A IP or better.

Course Description: Tutoring in computer science courses, especially introductory courses.

Prerequisite(s): Consent of instructor.

Course Description: Directed group study.

Prerequisite(s): Consent of instructor.

Course Description: Student facilitated course intended primarily for upper division students.

Prerequisite(s): Consent of instructor.

Course Description: Special study for advanced undergraduates.

Course Description: Under the supervision of a faculty member, an undergraduate student plans and develops the course they will offer under 098F/198F.

Course Description: STU FAC. Under the supervision of a faculty member, an undergraduate student teaches a course under 098F/198F.

Prerequisite(s): ECS 199FA; consent of instructor.

Course Description: Modern research topics and methods in computer architecture. Design implications of memory latency and bandwidth limitations. Performance enhancement via within-processor and between-processor parallelism.

Prerequisite(s): ECS 154A or EEC 170.

Course Description: Maximizing uniprocessor performance. Barriers to high performance; solutions to the problems; historical and current processor designs.

Prerequisite(s): ECS 201A.

Course Description: Evolution of parallel architectures from special-purpose machines to commodity servers. Emphasis on recent machines and applications that drive them.

Prerequisite(s): ECS 201A.

Course Description: Novel computing technologies that could revolutionize computer architecture. Quantum computing technologies, including algorithms, devices, and fault tolerance. A survey of other unconventional technologies including nanoscale electronics, MEMS devices, biological devices, and nanotechnology.

Prerequisite(s): ECS 201A.

Course Description: Time and space complexity classes. Reductions, completeness, and the role of randomness. Logic and undecidability.

Prerequisite(s): ECS 120; ECS 122A.

Course Description: Computational methods related to systems and synthetic biology. An overview of machine learning techniques related to the analysis of biological data, biological networks. Predictive modeling and simulation of biological systems. Topics on biological circuit construction.

Course Description: Techniques for designing efficient algorithms, analyzing their complexity and applying these algorithms to a broad range of applications. Methods for recognizing and dealing with difficult problems.

Prerequisite(s): ECS 122A; STA 031A recommended.

Course Description: Advanced topics in complexity theory. Problem classification. The classes P, NP, P-space, co-NP. Matching and network flow algorithms. Matrix multiplication. Approximation algorithms.

Prerequisite(s): ECS 222A.

Course Description: Models of parallel computer systems including PRAMs, loosely coupled systems and interconnection networks. Parallel algorithms for classical problems and general techniques for their design and analysis. Proving lower bounds on parallel computation in several settings.

Prerequisite(s): ECS 222A.

Course Description: Algorithms that operate on strings. Pattern matching, sets of patterns, regular expression pattern matching, suffix trees and applications, inexact similarity, parametric sequence alignment, applications to DNA sequencing and protein database searching.

Prerequisite(s): ECS 122A.

Course Description: Fundamental concepts. Planar graphs: Kuratowski's theorem. Packings and coverings. Menger's theorem, representation of cuts, Hamilton graphs, rigid graphs, chordal graphs, graph coloring, graph isomorphism, applications and some algorithms.

Prerequisite(s): graduate standing in electrical engineering or computer science or consent of instructor.

Course Description: Mathematics of unstructured data. Algorithms for data structures such as Voronoi diagrams, oct-trees, and arrangements. Applications in computer graphics, concentrating on problems in three-dimensions.

Prerequisite(s): ECS 175; ECS 222A.

Course Description: Modern cryptography as a discipline emphasizing formal definitions and proofs of security. One-way functions, pseudo-randomness, encryption, digital signatures, zero-knowledge, secure protocols.

Prerequisite(s): ECS 220 or ECS 222A.

Course Description: Cryptographic primitives and protocols of importance to e-commerce, present and future, including content distribution mechanisms, payment mechanisms, pricing mechanisms, anonymity and privacy mechanisms, fair exchange mechanisms.

Prerequisite(s): ECS 222A.

Course Description: Algorithmic problems in structural biology; protein structure classification; protein structure prediction (including comparative modeling and ab initio protein structure prediction); molecular simulations (molecular dynamics and Monte Carlo simulations).

Prerequisite(s): Graduate standing.

Course Description: Numerical linear algebra (NLA) with emphasis on applications in engineered systems; matrix factorizations; perturbation and rounding error analyses of fundamental NLA algorithms.

Prerequisite(s): ECS 130 or EAD 209 or MAT 167.

Course Description: Algorithms and techniques for large-scale scientific computation, including basics for high performance computing, iterative methods, discrete approximation, fast Fourier transform, Poisson solvers, particle methods, spectral graph partition and its applications.

Prerequisite(s): ECS 130.

Course Description: Theory of chemical reaction networks, molecular circuits, DNA self-assembly, DNA sequence design and thermodynamic energy models, and connections to the field of distributed computing.

Prerequisite(s): ECS 120 or ECS 220 recommended; probability course recommended, for example ECS 132 or MAT 135A or STA 012 or STA 131A or MAT 131A; or consent of instructor.

Course Description: Bioinformatics methods for analysis and inference of functional relationships among genes using large-scale genomic data, including methods for integration of gene expression, promoter sequence, TF-DNA binding and other data, and approaches in modeling of biological networks.

Prerequisite(s): ECS 124; graduate standing in Computer Science or Life Sciences.

Course Description: Modern topics in computer security, including: protection, access control, operating systems security, network security, applied cryptography, cryptographic protocols, secure programming practices, safe languages, mobile code, malware, privacy and anonymity, and case studies from real-world systems.

Prerequisite(s): ECS 150; ECS 152A recommended.

Course Description: Theoretical foundations of methods used to protect data in computer and communication systems. Access control matrix and undecidability of security; policies; Bell-LaPadula, Biba, Chinese Wall models; non-interference and non-deducibility; information flow and the confinement problem.

Prerequisite(s): ECS 235A; ECS 120 and ECS 150 recommended.

Course Description: Concepts of intrusion detection, anomaly detection based on machine learning, signature-based detection using pattern matching, automated response to attacks using artificial intelligence planning, tracing intruders based on principal component analysis, security policy languages.

Prerequisite(s): ECS 150; ECS 153 recommended.

Course Description: Advanced topics in programming languages, including formal syntax and semantics, the relation between formal semantics and verification, an introduction to the lambda calculus. Additional topics will include language design principles, alternative programming languages, in-depth semantic theory and models of language implementation.

Prerequisite(s): ECS 140A; ECS 142.

Course Description: Lexical analysis, parsing, storage management, symbol table design, semantic analysis and code generation. LR, LALR grammars. Compilercompilers.

Prerequisite(s): ECS 240.

Course Description: Compiler optimizations for performance, code size and power reduction. Topics include control- and data-flow analysis, redundancy elimination, loop and cache optimizations, register allocation, local and global instruction scheduling, and modulo scheduling.

Prerequisite(s): ECS 201A or EEC 270.

Course Description: Fundamental concepts and applications of concurrent programs; concurrent program verification and derivation; synchronization mechanisms in programming languages; distributed programming techniques; case studies of languages.

Prerequisite(s): ECS 020; ECS 150.

Course Description: Techniques for the analysis of software artifacts, including source code, build processes, test suites, bug reports and documentation with a special emphasis on software correctness and performance in real-world settings.

Prerequisite(s): ECS 140A; ECS 142.

Course Description: Language design parameters. Models of parallel machines. Load balancing. Scalability. Portability. Efficiency measures. Design and implementation techniques for several classes of concurrent programming languages (such as object-oriented, functional, logic, and constraint programming languages).

Prerequisite(s): ECS 140A; ECS 150.

Course Description: Models, design, implementation, performance evaluation in operating systems. Algorithms, internal architectures for single processor OS and distributed systems. Concurrency control, recovery, security. OS kernel-level programming. Special topics embedded systems, real-time system, device driver, NPU (Network Processor Unit).

Prerequisite(s): ECS 150.

Course Description: Internet protocol based computer networks applications, transport, network layer protocols. High speed LAN technologies: Ethernet, Asynchronous Transfer Mode (ATM). Delay models in data networks: analysis of multiaccess techniques in polling, ring, random access networks. Multimedia applications requirements and design.

Prerequisite(s): ECS 152B.

Course Description: Develops the mathematical theory underlying growth, structure and function of networks with applications to physical, social, biological and engineered systems. Topics include network growth, resilience, epidemiology, phase transitions, software and algorithms, routing and search control, cascading failures.

Prerequisite(s): MAT 022A; MAT 022B; (STA 013 or STA 013Y or STA 120); experience with computer software, or consent of instructor.

Course Description: Advanced research issues in wireless communication networks, including multi-user diversity and cross-layer optimization, basic network information theory, MIMO systems and the impact on networks, and dynamics spectrum management.

Prerequisite(s): ECS 252A recommended.

Course Description: Probabilistic and statistical models useful in computer/data science. Applications to networks, bioinformatics, database management, machine learning, software engineering and image processing.

Prerequisite(s): A calculus-based course in probability, such as ECS132, STA 131A, or EEC 161; programming skills and familiarity with matrix algebra.

Course Description: Fundamental techniques in design of second generation wireless networks: cellular network and protocols, medium access techniques, handoff control, signaling and mobility management, wireless data works, Internet mobility and Personal Communication Services (PCS). Third generation wideband systems, novel technologies, adhoc networks.

Prerequisite(s): ECS 252.

Course Description: Optical networks. Enabling technologies. Multiplexing techniques. WDM. Broadcast networks. Wavelength-routed networks. Network architectures. Protocols. Network algorithms. Device-network interface. Optimization problems.

Prerequisite(s): ECS 252.

Course Description: Advanced techniques for domain-specific software reuse.

Prerequisite(s): ECS 142; ECS 160 recommended.

Course Description: Methods of proving correctness of programs with respect to formal specifications, with attention to those suited for employing automated deduction.Logic background, symbolic execution, techniques suited to iterative programming, methods from denotational semantics, termination, dynamic logic and proofs of concurrent programs.

Prerequisite(s): PHI 112; or MAT 125 or familiarity with first-order logic; knowledge of an integrative and functional programming language.

Course Description: Formal specification of modules, and its relationship to topdown programming development and verification. Abstract data types, together with methods for specifying them. Implementations and proofs of implementation. Using specifications to reason about programs. Parameterized types. Constructing good formal specifications.

Prerequisite(s): ECS 261.

Course Description: Concepts of distributed database systems and architectures, distributed database design, distributed query processing and optimization, transaction management and concurrency control, heterogeneous and multidatabase systems.

Prerequisite(s): ECS 165A.

Course Description: Concepts, models, and architectures for spatial databases, spatial access methods, query processing, spatio-temporal data management, moving objects, spatial data mining.

Prerequisite(s): ECS 165A.

Course Description: Wide-area distributed information systems, data broadcast, multicast, publish/subscribe, service differentiation, information retrieval, Web caching.

Prerequisite(s): ECS 152B or ECS 165A.

Course Description: Scientific data integration, metadata, knowledge representation, ontologies, scientific workflow design and management.

Prerequisite(s): ECS 165A.

Course Description: Graduate seminar course on computer vision with an emphasis on object recognition, activity recognition, and scene understanding.

Prerequisite(s): ECS 171 or ECS 174; or equivalent.

This version has ended; see updated course, below.
Course Description: Concepts and techniques underlying the design and implementation of models of human performance on intelligent tasks. Representation of high-level knowledge structures. Models of memory and inference. Natural language and story understanding. Common sense planning and problem solving.

Prerequisite(s): ECS 140A; ECS 172 recommended.

Course Description: Artificial intelligence techniques for knowledge acquisition by computers. Fundamental problems in machine learning and discovery. Systems that learn from examples, analogies, and solved problems. Systems that discover numerical laws and qualitative relationships. Projects centering on implementation and evaluation.

Prerequisite(s): ECS 170.

Course Description: Advanced topics in information visualization: perceptually effective display methods, color design and selection, interaction models and techniques, focus-context techniques, distortion methods, large graph visualization techniques, visual data mining methods, and evaluation methods.

Prerequisite(s): ECS 163 or ECS 175 recommended.

Course Description: Analytical reasoning using visual means, data and visual transformations, exploratory visualization, explanatory visualization, interactive intelligent systems, qualitative and quantitative evaluation.

Prerequisite(s): Graduate standing.

Course Description: Techniques of mechanical theorem proving. Methods based on resolution and termrewriting. Decision procedures. Induction. Applications to program verification, question/answering and plan generation. Study existing mechanical theorem provers.

Prerequisite(s): PHI 112; or MAT 125 or familiarity with first order logic.

Course Description: Advanced topics in computer graphics. Hidden surface models, rendering of various surface types, subdivision methods, shading techniques, anti-aliasing, modeling techniques.

Prerequisite(s): ECS 175 or ECS 177 or ECS 178.

Course Description: Advanced topics in computer graphics and geometric modeling. Topics taken from advanced research papers in computer graphics, image synthesis, visualization and geometric modeling. Discussion of current research in the field.

Prerequisite(s): ECS 175 or ECS 177 or ECS 178.

Course Description: Applications, available tools and techniques, the challenges confronting the field of volume visualization, and some of the advanced topics in the field. Primary emphasis on advanced software and hardware techniques to achieve interactive visualization.

Prerequisite(s): ECS 177.

Course Description: Visualization of 3D data, including scalar fields, vector fields, and medical data.

Prerequisite(s): ECS 177.

Course Description: Mathematical techniques for the definition and manipulation of curves and surfaces. Bezier curves and surfaces, B-spline curves and surfaces, subdivision surfaces, wavelets. Integration into various computer graphics rendering models, visualization systems and computer-aided design systems.

Prerequisite(s): ECS 175.

Course Description: Surveys current research and fundamental techniques that lie behind character animation tools. Emphasis on improving expressive aspects of movement and how physics, motion capture data, the arts and psychology literature, and interactive techniques can be used towards this goal.

Prerequisite(s): ECS 175; or ECS 275.

Course Description: Fundamentals and principles of Virtual Reality (VR) technology. Potential and limits for its useful application. Developing a complete virtual reality application.

Prerequisite(s): ECS 175.

Course Description: Special topic in Computer Science Theory.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Architecture.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Programming Languages & Compilers.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Operating Systems.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Software Engineering.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Databases.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Artificial Intelligence.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer Graphics.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Networks.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer-Aided Design.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Scientific Computing.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Computer Science.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Security.

Prerequisite(s): Consent of instructor.

Course Description: Special topic in Bioinformatics & Computational Biology.

Prerequisite(s): Consent of instructor.

Course Description: Participating seminar; discussion and presentation of current research and development in computer science.

Course Description: Research problems, progress and techniques in computer science.

Course Description: Study of research topics in computer science, Ph.D. level research methodologies (experimental, applied and theoretical). Study skills necessary to successfully find/solve significant research problems. Finding and successful interacting with a research advisor. Ethical issues in research/collaborative work.

Prerequisite(s): Graduate standing in computer science.

Course Description: Study of Ph.D. level research methodologies (experimental, applied and theoretical), presenting research results for the computer science community. Study skills necessary to successfully find/solve significant research problems.

Prerequisite(s): Graduate standing in computer science; ECS 293A recommended.