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Requirements for the MS degree programs changed effective Fall 2015. Students
entering in Fall 2015 must follow the new curriculum. Continuing students may
remain in their current curriculum or may change to the Fall 2015
requirements.
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The masters degree is offered with the title Computer Science and Engineering
or Computer Science and Engineering (Computer Engineering).
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Students must register for a minimum of three quarters for residency
requirements. To maintain good academic standing, students must be making timely
and satisfactory progress toward completion of degree requirements and must
maintain a minimum overall GPA of 3.0 at UC San Diego.
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M.S. Plan I - Thesis
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M.S. Plan II- Comprehensive Exam, Standard Option
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M.S. Plan II - Comprehensive Exam, Interdisciplinary Option
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CSE 200. Computability and Complexity (4)
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Computability review, including halting problem, decidable sets, r.e. sets,
many-one reductions; TIME(t(n)), SPACE(s(n)) and general relations between these
classes; L, P, PSPACE, NP; NP—completeness; hierarchy theorems; RP, BPP.
Prerequisites: CSE 105 or equivalent.
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CSE 201A. Advanced Complexity (4)
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Polynomial-time hierarchy (PH); BPP in second level of PH; Savitch's theorem;
NL=coNL; non-uniform and circuit complexity; some circuit lower bounds;
IP=PSPACE; probabilistic proof checking (PCP); Application of PCP to
approximation hardness; Complexity of proof systems; Parallel complexity classes
NC and AC; P-completeness. Recommended preparation: CSE 200 is recommended.
Prerequisites: graduate standing.
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CSE 202. Algorithm Design and Analysis (4)
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The basic techniques for the design and analysis of algorithms.
Divide-and-conquer, dynamic programming, data structures, graph search,
algebraic problems, randomized algorithms, lower bounds, probabilistic analysis,
parallel algorithms. Prerequisites: CSE 101 or equivalent.
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CSE 203A. Advanced Algorithms (4)
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Modern advances in design and analysis of algorithms. Exact syllabus varies.
Topics include approximation, randomized algorithms, probabilistic analysis,
heuristics, online algorithms, competitive analysis, models of memory hierarchy,
parallel algorithms, number-theoretic algorithms, cryptanalysis, computational
geometry, computational biology, network algorithms, VLSI CAD algorithms.
Prerequisites: CSE 202.
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CSE 205A. Logic in Computer Science (4)
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(Formerly CSE 208D) Mathematical logic as a tool in computer science.
Propositional logic, resolution, first-order logic, completeness and
incompleteness theorems with computational viewpoint, finite model theory,
descriptive complexity, logic programming, nonmonotonic reasoning, temporal
logic. Applications to databases, automatic theorem proving, program
verification, and distributed systems. Prerequisites: CSE 200 or consent of
instructor.
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CSE 206A. Lattice Algorithms and Applications (4)
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(Formerly CSE 207C) Introduction to the algorithmic theory of point lattices
(aka algorithmic geometry of numbers), and some of its most important
applications in cryptography and cryptanalysis. Topics usually include: LLL
basis reduction algorithm, cryptanalysis of broadcast RSA, hardness of
approximating lattice problems. Prerequisites: CSE 202, CSE 200, or
concurrent.
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CSE 207. Modern Cryptography (4)
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Private and public key cryptography, introduction to reduction based proofs
of security, concrete security, block ciphers, pseudorandom functions and
generators, symmetric encryption, asymmetric encryption, computational number
theory, RSA and discrete log systems, message authentication, digital
signatures, key distribution and key management. Prerequisites: CSE 202 or
consent of instructor.
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CSE 208. Advanced Cryptography (4)
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Zero-knowledge, secure computation, session-key distribution, protocols,
electronic payment, one-way functions, trapdoor permutations, pseudorandom bit
generators, hardcore bits. Prerequisites: CSE 202, CSE 200, and CSE 207 or
consent of instructor.
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CSE 209A. Topics/Seminar in Algorithms, Complexity, and Logic (1–4)
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Topics of special interest in algorithms, complexity, and logic to be
presented by faculty and students under faculty direction. Topics vary from
quarter to quarter. May be repeated for credit. Prerequisites: consent of
instructor.
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CSE 209B. Topics/Seminar in Cryptography (1–4)
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Topics of special interest in cryptography to be presented by faculty and
students under faculty direction. Topics vary from quarter to quarter. May be
repeated for credit. Prerequisites: consent of instructor.
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CSE 210. Principles of Software Engineering (4)
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(Formerly CSE 264A.) General principles in modern software engineering. Both
theoretical and practical topics are covered. Theoretical topics include proofs
of correctness, programming language semantics, and theory of testing. Practical
topics include structured programming, modularization techniques, design of
languages for reliable programming, and software tools. Prerequisites: CSE 100,
131A, 120, or consent of instructor.
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CSE 211. Software Testing and Analysis (4)
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Survey of testing and analysis methods. Introduction to advanced topics in
area as well as traditional production methods. Topics include inspections and
reviews, formal analysis, verification and validation standards, nonstatistical
testing, statistical-testing and reliability models, coverage methods, testing
and analysis tools, and organization management and planning. Methods special to
special development approaches such as object-oriented testing will also be
described. Prerequisites: undergraduate major in computer science or extensive
industrial experience.
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CSE 216. Research Topics in Human-Computer Interaction (4)
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Prepares students to conduct original HCI research by reading and discussing
seminal and cutting-edge research papers. Topics include design, social
software, input techniques, mobile, and ubiquitous computing. Student pairs
perform a quarter-long mini research project that leverages campus research
efforts. Cross-listed with COGS 230. Prerequisites: none.
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CSE 218. Advanced Topics in Software Engineering (4)
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This course will cover a current topic in software engineering in depth.
Topics in the past have included software tools, impacts of programming language
design, and software system structure. (S/U grades permitted.) Prerequisites:
none.
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CSE 219. Design at Large (1)
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New societal challenges, cultural values, and technological opportunities are
changing design—and vice versa. The seminar explores this increased scale,
real-world engagement, and disruptive impact. Invited speakers from UC San Diego
and beyond share cutting-edge research on interaction, design, and learning.
Cross-listed with COGS 229. (S/U grades only.) Prerequisites: none.
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CSE 221. Operating Systems (4)
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Operating system structures, concurrent computation models, scheduling,
synchronization mechanisms, address spaces, memory management protection and
security, buffering, streams, data-copying reduction techniques, file systems,
naming, caching, disk organization, mapped files, remote file systems, case
studies of major operating systems. Prerequisites: CSE 120 and 121, or consent
of instructor.
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CSE 222A. Computer Communication Networks (4)
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(Formerly CSE 222.) Computer communication network concepts, protocols, and
architectures, with an emphasis on an analysis of algorithms, protocols, and
design methodologies. Topics will include layering, error control, flow control,
congestion control, switching and routing, quality of service management,
mobility, naming, security, and selected contemporary topics. Prerequisites: CSE
123A or consent of instructor.
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CSE 222B. Internet Algorithmics (4)
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(Formerly CSE 228H.) Techniques for speeding up Internet implementations,
including system restructuring, new algorithms, and hardware innovations. Topics
include: models for protocols, systems and hardware; efficiency principles;
applying these principles to deriving techniques for efficient implementation of
common endnode and router functions. Prerequisites: CSE 123A or CSE 222A, or
consent of instructor.
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CSE 223A. Principles of Distributed Computing
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Former CSE Course is no longer offered.
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CSE 223B. Distributed Computing and Systems (4)
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Efficient primitives for distributed operating systems and high-performance
network servers, including concurrent and event-driven server architectures,
remote procedure calls, and load shedding. Distributed naming, directory, and
storage services, replication for fault tolerance, and security in distributed
systems. Prerequisites: CSE 221, CSE 222A, or consent of instructor.
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CSE 227. Computer Security (4)
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Security and threat models, risk analysis, authentication and authorization,
auditing, operating systems security, access control mechanisms, protection
mechanisms, distributed systems/network security, security architecture,
electronic commerce security mechanisms, security evaluation. Prerequisites: CSE
221 or consent of instructor.
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CSE 229A. Topics/Seminar in Computer Systems (1–4)
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Discussion on problems of current research interest in computer systems.
Possible areas of focus include: distributed computing, computational grid,
operating systems, fault-tolerant computing, storage systems, system services
for the World Wide Web. Topics to be presented by faculty and students under
faculty direction. Topics vary from quarter to quarter. May be repeated for
credit. Prerequisites:consent of instructor.
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CSE 229B. Topics/Seminar in Networks and Communication
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Former CSE Course is no longer offered.
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CSE 229C. Topics/Seminar in Computer Security (1–4)
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Discussion on problems of current research interest in computer security.
Topics to be presented by faculty and students under faculty direction. Topics
vary from quarter to quarter. May be repeated for credit. Prerequisites: consent
of instructor.
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CSE 230. Principles of Programming Languages (4)
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(Formerly CSE 273.) Functional versus imperative programming. Type systems
and polymorphism; the ML language. Higher order functions, lazy evaluation.
Abstract versus concrete syntax, structural and well-founded induction. The
lambda calculus, reduction strategies, combinators. Denotational semantics,
elementary domain theory. Prerequisites: CSE 130 or equivalent, or consent of
instructor.
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CSE 231. Advanced Compiler Design (4)
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(Formerly CSE 264C.) Advanced material in programming languages and
translator systems. Topics include compilers, code optimization, and debugging
interpreters. Prerequisites: CSE 100, 131A–B, or consent of instructor.
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CSE 232. Principles of Database Systems (4)
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(Formerly CSE 264D.) Database models including relational, hierarchic, and
network approaches. Implementation of databases including query languages and
system architectures. Prerequisites: CSE 100 or consent of instructor.
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CSE 232B. Database System Implementation (4)
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A hands-on approach to the principles of databases implementation. Algebraic
rewriters/optimizers, query processors, triggers. Beyond centralized relational
databases. Prerequisites: CSE 232.
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CSE 233. Database Theory (4)
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Theory of databases. Theory of query languages, dependency theory, deductive
databases, incomplete information, complex objects, object-oriented databases,
and more. Connections to logic and complexity theory including finite model
theory and descriptive complexity. Prerequisites: CSE 200.
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CSE 237A. Introduction to Embedded Computing (4)
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Embedded system technologies including processors, DSP, memory, and software.
System interfacing basics, communication strategies, sensors, and actuators.
Mobile and wireless technology in embedded systems. Using predesigned hardware
and software components. Design case studies in wireless, multimedia, and/or
networking domains. Prerequisites: basic courses in digital hardware, algorithms
and data structures, elementary calculus, and probability; or consent of
instructor.
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CSE 237B. Software for Embedded Systems (4)
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Embedded computing elements, device interfaces, time-critical IO handling.
Embedded software design under size, performance, and reliability constraints.
Software timing and functional validation. Programming methods and compilation
for embeddable software. Embedded runtime systems. Case studies of real-time
software systems. Prerequisites: CSE 237A; or basic courses in programming,
algorithms and data structures, elementary calculus, discrete math, computer
architecture; or consent of instructor.
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CSE 237C. Validation and Testing of Embedded Systems (4)
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Embedded system building blocks including IP cores. Cosimulation. Formal
verification using model checking. Verification environments. Test challenges in
core integration: compliance, feature, random, and collision testing. Core
access and test integration. Interface-based verification and standards.
Prerequisites: CSE 237A; or basic courses in algorithms and data structures,
elementary calculus, discrete math, symbolic logic, computer architecture; or
consent of instructor.
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CSE 237D. Design Automation and Prototyping for Embedded Systems (4)
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System representation and modeling. Abstract and language models. Simulation
as a modeling activity. Computational and hw/sw system prototypes. System
analysis using models. Constraint and interface modeling. Behavioral compilation
and synthesis. Prerequisites: CSE 237A; or basic courses in digital logic
design, algorithms and data structures, elementary calculus, discrete math,
symbolic logic, computer architecture; or consent of instructor.
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CSE 239A. Topics/Seminar in Databases (1–4)
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Discussion on problems of current research interest in databases. Possible
areas of focus include: core database issues, data management on the web, data
integration, new database models and applications, formal methods in databases.
Topics to be presented by faculty and students under faculty direction. Topics
vary from quarter to quarter. May be repeated for credit. Prerequisites: consent
of instructor.
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CSE 240A. Principles of Computer Architecture (4)
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(Formerly CSE 240.) This course will cover fundamental concepts in computer
architecture. Topics include instruction set architecture, pipelining, pipeline
hazards, bypassing, dynamic scheduling, branch prediction, superscalar issue,
memory-hierarchy design, advanced cache architectures, and multiprocessor
architecture issues. Prerequisites: CSE 141 or consent of instructor.
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CSE 240B. Parallel Computer Architecture (4)
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This course covers advanced topics in parallel computer architecture,
including on-chip and off-chip interconnection networks, cache coherence, cache
consistency, hardware multithreading, multi-core and tiled architectures. It
incorporates the latest research and development on parallel architectures and
compilation techniques for those architectures. CSE 240A recommended.
Prerequisites:graduate standing.
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CSE 240C. Advanced Microarchitecture (4)
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This course covers advanced topics in computer architecture. It incorporates
the latest research and development on topics such as branch prediction,
instruction-level parallelism, cache hierarchy design, speculative
multithreading, reliable architectures, and power-management techniques. CSE
240A recommended. Prerequisites: graduate standing.
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CSE 240D. Application Specific Processors
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Former CSE Course is no longer offered.
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CSE 241A/ECE 260B. VLSI Integration of Computing Circuitry (4)
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VLSI integrated-circuit building blocks of computing systems, and their
implementation. Computer-aided design and performance simulations, design
exercises and projects. Devices, standard cells and interconnects, clocking,
power/ground distribution, arithmetic modules, memories. Methodologies and
tradeoffs in system implementation. Prerequisites: layout (CSE 165 or ECE 260A)
and logic design (CSE 140 or ECE 111), or consent of instructor.
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CSE 242A. Integrated Circuit Layout Automation
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Former CSE Course is no longer offered.
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CSE 243A. Introduction to Synthesis Methodologies in VLSI CAD (4)
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Hardware software co-design, architectural level synthesis, control synthesis
and optimization, scheduling, binding, register and bus sharing, interconnect
design, module selection, combinational logic optimization, state minimization,
state encoding, and retiming. Prerequisites: none.
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CSE 244A. VLSI Test (4)
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Design for test, testing economics, defects, failures and faults, fault
models, fault simulation, automatic test pattern generation, functional testing,
memory, PLA, FPGA, microprocessor test, and fault diagnosis. Prerequisites:
none.
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CSE 245. Computer Aided Circuit Simulation and Verification (4)
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This course is about the computer algorithms, techniques, and theory used in
the simulation and verification of electrical circuits. Prerequisites: CSE 241A
or consent of instructor.
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CSE 247. Application Specific and Reconfigurable Computer Architecture
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Former CSE Course is no longer offered.
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CSE 248. Algorithmic and Optimization Foundations for VLSI CAD (4)
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Algorithmic techniques and optimization frameworks for large-scale, difficult
optimizations. Primal-dual multicommodity flow approximations, approximations
for geometric and graph Steiner formulations, continuous placement optimization,
heuristics for Boolean satisfiability, multilevel methods, semidefinite
programming, and application to other formulations (e.g., scheduling).
Prerequisites: CSE 241A or CSE 242A, or consent of instructor.
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CSE 249A. Topics/Seminar in Computer Architecture (1–4)
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Topics of special interest in computer architecture to be presented by
faculty and students under faculty direction. Topics vary from quarter to
quarter. May be repeated for credit. Prerequisites: consent of
instructor.
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CSE 249B. Topics/Seminar in VLSI (1–4)
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Topics of special interest in VLSI to be presented by faculty and students
under faculty direction. Topics vary from quarter to quarter. May be repeated
for credit. Prerequisites: consent of instructor.
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CSE 249C. Topics/Seminar in CAD
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Former CSE Course is no longer offered.
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CSE 250A. Principles of Artificial Intelligence: Probabilistic Reasoning and
Learning (4)
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Methods based on probability theory for reasoning and learning under
uncertainty. Content may include directed and undirected probabilistic graphical
models, exact and approximate inference, latent variables,
expectation-maximization, hidden Markov models, Markov decision processes,
applications to vision, robotics, speech, and/or text. Recommended preparation:
CSE 103 or similar course. Prerequisites: graduate standing in CSE or consent of
instructor.
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CSE 250B. Principles of Artificial Intelligence: Learning Algorithms
(4)
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Algorithms for supervised and unsupervised learning from data. Content may
include maximum likelihood; log-linear models, including logistic regression and
conditional random fields; nearest neighbor methods; kernel methods; decision
trees; ensemble methods; optimization algorithms; topic models; neural networks;
and backpropagation. Recommended preparation: CSE 103 or similar course.
Prerequisites: graduate standing or consent of instructor.
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CSE 250C. Machine Learning Theory (4)
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Theoretical foundations of machine learning. Topics include concentration of
measure, the PAC model, uniform convergence bounds, and VC dimension. Possible
topics include online learning, learning with expert advice, multiarmed bandits,
and boosting. Recommended preparation: CSE 103 and CSE 101 or similar course.
Prerequisites: graduate standing or consent of instructor.
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CSE 252A. Computer Vision I (4)
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Comprehensive introduction to computer vision providing broad coverage
including low-level vision (image formation, photometry, color, image feature
detection), inferring 3-D properties from images (shape-from shading, stereo
vision, motion interpretation) and object recognition. Companion to CSE 252B
covering complementary topics. Prerequisites: Math 10D and Math 20A–F or
equivalent.
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CSE 252B. Computer Vision II (4)
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Comprehensive introduction to computer vision providing focused coverage of
multiview geometry, structure from motion, image segmentation, motion
segmentation, texture analysis and recognition, object detection, and
image-based rendering. Companion to CSE 252A covering complementary topics.
Prerequisites: Math 10D and Math 20A–F or equivalent.
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CSE 252C. Selected Topics in Vision and Learning (1–4)
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Selected topics in computer vision and statistical pattern recognition, with
an emphasis on recent developments. Possible topics include: grouping and
segmentation, object recognition and tracking, multiple view geometry,
kernel-based methods, dimensionality reduction, and mixture models.
Prerequisites: CSE 252 or equivalent and CSE 250B or equivalent.
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CSE253 - Neural Networks/Pattern Recognition
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Probability density estimation, perceptrons, multilayer neural networks,
radial basis function networks, support vector machines, error functions, data
preprocessing. Possible topics include unsupervised learning methods, recurrent
networks, and mathematical learning theory. Recommended preparation: CSE 250B or
equivalent. Prerequisites: graduate standing.
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CSE 254. Statistical Learning (4)
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Learning algorithms based on statistics. Possible topics include
minimum-variance unbiased estimators, maximum likelihood estimation, likelihood
ratio tests, resampling methods, linear logistic regression, feature selection,
regularization, dimensionality reduction, manifold detection. An upper-division
undergraduate course on probability and statistics such as Math 183 or 186, or
any graduate course on statistics, pattern recognition, or machine learning is
recommended. Prerequisites: graduate standing.
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CSE 255. Data Mining and Predictive Analytics (4)
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Learning methods for applications. Content may include data preparation,
regression and classification algorithms, support vector machines, random
forests, class imbalance, overfitting, decision theory, recommender systems and
collaborative filtering, text mining, analyzing social networks and social
media, protecting privacy, A/B testing. Recommended preparation: CSE 103 or
similar. Prerequisites:graduate standing or consent of instructor.
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CSE 256/LING 256. Statistical Natural Language Processing
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Former CSE Course is no longer offered.
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CSE258 - Recommender Systems & Web Mining
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Current methods for data mining and predictive analytics. Emphasis is on
studying real-world data sets, building working systems, and putting current
ideas from machine learning research into practice. Prerequisites: graduate
standing.
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CSE 258A. Cognitive Modeling (4)
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Connectionist models and a sampling of other cognitive modeling techniques.
Models of language processing, memory, sequential processes, and vision. Areas
covered may vary depending on student and faculty interests. Can be repeated for
credit. CSE 151 or CSE 250B or CSE 253 or CSE 254, or equivalent experience
recommended. Prerequisites: graduate standing.
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CSE 259. Seminar in Artificial Intelligence (1)
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A weekly meeting featuring local (and occasional external) speakers
discussing their current research in Artificial Intelligence Neural Networks,
and Genetic Algorithms. (S/U grades only.) Prerequisites: none.
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CSE 259C. Topics/Seminar in Machine Learning
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Former CSE Course is no longer offered.
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CSE 260. Parallel Computation (4)
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(Formerly CSE 274A.) This course provides an overview of parallel hardware,
algorithms, models, and software. Topics include Flynn’s taxonomy,
interconnection networks, memory organization, a survey of commercially
available multiprocessors, parallel algorithm paradigms and complexity criteria,
parallel programming environments and tools for parallel debugging, language
specification, mapping, performance, etc. Prerequisites: graduate standing or
consent of instructor.
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CSE 262. System Support for Applications of Parallel Computation (4)
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This course will explore design of software support for applications of
parallel computation. Topics include: programming languages, run time support,
portability, and load balancing. The course will terminate in a project.
Prerequisites: consent of instructor.
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CSE 272. Advanced Image Synthesis (4)
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Computer graphics techniques for creating realistic images. Topics include
ray tracing, global illumination, subsurface scattering, and participating
media. CSE 168 or equivalent recommended.
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CSE 274. Selected Topics in Graphics (2–4)
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Selected topics in computer graphics, with an emphasis on recent
developments. Possible topics include computer animation, shape modeling and
analysis, image synthesis, appearance modeling, and real-time rendering. CSE 168
or CSE 169 recommended. Prerequisites:graduate standing or consent of
instructor.
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CSE 280A. Algorithms in Computational Biology (4)
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(Formerly CSE 206B.) The course focuses on algorithmic aspects of modern
bioinformatics and covers the following topics: computational gene hunting,
sequencing, DNA arrays, sequence comparison, pattern discovery in DNA, genome
rearrangements, molecular evolution, computational proteomics, and others.
Prerequisites: CSE 202 preferred or consent of instructor.
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CSE 282/BENG 202. Bioinformatics II: Sequence and Structure Analysis—Methods
and Applications (4)
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(Formerly CSE 257A/BENG 202.) Introduction to methods for sequence analysis.
Applications to genome and proteome sequences. Protein structure,
sequence-structure analysis. Prerequisites: Pharm 201 or consent of
instructor.
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CSE 283/BENG 203. Bioinformatics III: Functional Genomics (4)
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Annotating genomes, characterizing functional genes, profiling,
reconstructing pathways. Prerequisites: Pharm 201, BENG 202/CSE 282, or consent
of instructor.
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CSE 290. Seminar in Computer Science and Engineering (1–4)
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(Formerly CSE 280A.) A seminar course in which topics of special interest in
computer science and engineering will be presented by staff members and graduate
students under faculty direction. Topics vary from quarter to quarter. May be
repeated for credit. (S/U grades only.) Prerequisites: consent of instructor.
(Offered as faculty resources permit.)
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CSE 291. Topics in Computer Science and Engineering (1–4)
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Topics of special interest in computer science and engineering. Topics may
vary from quarter to quarter. May be taken for credit nine times with the
consent of instructor. Prerequisites: consent of instructor. (S/U grades
permitted.) (Offered as faculty resources permit.)
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CSE 292. Faculty Research Seminar (1)
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(Formerly CSE 282.) Computer science and engineering faculty will present
one-hour seminars of the current research work in their areas of interest.
Prerequisites: CSE graduate status.
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CSE 293. Special Project in Computer Science and Engineering (1–12)
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The student will conceive, design, and execute a project in computer science
under the direction of a faculty member. The project will typically include a
large programming or hardware design task, but other types of projects are
possible. Prerequisites: CSE graduate student status. (CS 75, 76, 77, 78, 79,
80, 81) (S/U grades only.)
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CSE 294. Research Meeting in CSE (2)
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Advanced study and analysis of active research in computer science and
computer engineering. Discussion of current research and literature in the
research specialty of the staff member teaching the course. Prerequisites:
consent of instructor.
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CSE 298. Independent Study (1–16)
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Open to properly qualified graduate students who wish to pursue a problem
through advanced study under the direction of a member of the staff. (S/U grades
only.) Prerequisites: consent of instructor.
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CSE 299. Research (1–16)
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Research. Prerequisites: consent of faculty.
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CSE 500. Teaching Assistantship (2–4)
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A course in which teaching assistants are aided in learning proper teaching
methods by means of supervision of their work by the faculty: handling of
discussions, preparation and grading of examinations and other written
exercises, and student relations. May be used to meet teaching experience
requirement for candidates for the PhD degree. Number of units for credit
depends on number of hours devoted to class or section assistance.
Prerequisites: graduate standing and consent of instructor.
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CSE 599. Teaching Methods in Computer Science (2)
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Training in teaching methods in the field of computer science. This course
examines theoretical and practical communication and teaching techniques
particularly appropriate to computer science. Prerequisites: consent of
faculty.