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工程与应用科学

计算机科学与工程

MS in Computer Science and Engineering

加州大学圣地亚哥分校

学院名称

暂无

专业编号

标准考试成绩要求
  • TOEFL80  

专业排名

计算机科学与工程专业排名暂无排名 ,US News 2018

招生人数

全球范围内招收学生暂无,秋季 2018

学年学费

$11,502.00

奖学政策

提供奖学金

学年学制

three quarters

所在校区

暂无

录取要求

To be considered for admission, applicants must meet the minimum university and departmental requirements outlined below. All application documents are submitted to the Graduate Division Online application at UCSD Application for Graduate Admission . For a complete list of all of the supplemental materials required for the graduate application, please visit our Graduate Application Checklist .

Additional questions about admission into our graduate program may be answered on our Graduate Admissions FAQ page. Please also see the Prospective Student web pages of the Graduate Division .

Prerequisites: A bachelor's degree in computer science, computer engineering, electrical engineering, or mathematics is preferred, but not required. Applicants with a degree in another discipline will be considered for admission if they have completed the minimum required CSE courses or their equivalent. However, it is recommended that applicants without a CSE background take courses beyond the minimum to demonstrate an ability to understand more advanced concepts in computer science and engineering: My bachelors degree isn't in CS. What kind of background should I have before applying to the program?

Academic and GPA: Applicants must hold a bachelor's degree or the equivalent from an accredited institution in the United States or from a recognized university-level academic institution abroad. At least a B average (3.0 GPA) or its equivalent is required for admission. Satisfaction of minimal standards does not, however, guarantee admission, since the number of qualified applicants far exceeds the number of places available. International and U.S. Applicants should refer to the UCSD Academic Policies - Admissions.

GRE: The General Test of the Graduate Record Examination (GRE) is REQUIRED OF ALL APPLICANTS. Applicants should request that ETS submit the scores directly to the UCSD institution code 4836. When ordering your GRE score reports, use UCSD's institution code 4836, the department codes are not necessary. Information about the GRE is available from the Educational Testing Service (ETS) website. Note: An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program.

TOEFL: The Test of English as a Foreign Language (TOEFL) is required for international applicants whose native language is not English and who have not studied full-time at a university-level institution in an English-speaking country for one uninterrupted academic year. ***No other test of English may be used to substitute for TOEFL for the CSE Graduate Application.

The UCSD TOEFL Institution Code is 4836 The university minimum TOEFL score required for admission is 550 for the paper-and-pencil version, 213 for the computer-based test or 80 for the internet-based test (iBT). For more information on the TOEFL, visit the TOEFL website . The Test of Spoken English (TSE) is not required. An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program.

DUPLICATE DEGREES: NOTICE - Normally, UCSD does not permit the duplication of advanced degrees. (Previous professional degrees, however, are not included in this restriction.) Holders of a master's degree in one field may be considered under certain circumstances for admission into a master's degree program in another field (after the admissions faculty committee reviews all application files in any given admissions cycle). Holders of a PhD, in any field, are advised not to apply for admission to the CSE department's PhD program.

申请材料清单

立即申请

The FALL 2018 application deadline is December 18, 2017.

Below is a list of the required materials that must be submitted as part of the graduate application to the MS or the PhD program.

CURRENT UCSD STUDENTS who wish to TRANSFER from their current graduate program to a different program (e.g., MS to PhD) should contact their student affairs staff adviser for more information on that process. Current UCSD CSE undergraduate majors who wish to pursue a CSE masters should consider the Bachelors/Masters program.

1. Online Application: UCSD Graduate Division Online Application

Applicants should review the Applying to Graduate Admission s prior to submitting their application.

The FALL 2018 application deadline is December 18, 2017

**All application documents are submitted online to the Graduate Student Online Application at https://apply.grad.ucsd.edu (please do not mail documents to the department).

If you would like to request a CSE department fee waiver, please fill out the Fee Waiver Application .

Application Status Checks: All applicants will receive a final decision via the online application by April. Applicants may check their online application for updates. Due to the high volume of inquiries, application status checks prior to April will not receive responses.

2. Official Transcripts

Transcripts should be uploaded on-line to the UCSD Online Graduate Application .

Review the Graduate Division transcript instructions carefully - Academic Records and Transcripts

Please do not mail any documents to the department. If offered admission, students will mail the official transcripts to the Graduate Division.

If a student is offered admission to the program: Official transcripts of record from each university-level institution attended must be provided. A summary of credit transferred from an institution previously attended and recorded on the transcript issued by the school granting the degree will not suffice. Applicants should request that official transcripts of all previous academic work, including certification of degrees received or documentation of status upon leaving each institution, be mailed to the Graduate Division. Only official records bearing the signature of the registrar and the seal of the issuing institution will be accepted.

Applicants who attended any campus of the University of California, including UCSD, must provide official transcripts of the UC course work. Transcripts from UCSD may be ordered by an applicant from the Office of the Registrar. There is no charge for UCSD transcripts of record sent to departments in support of an application for graduate study.

International applicants:

If a student is offered admission to the program: True copies, facsimiles, or photostatic copies of foreign academic records will be accepted if, after the copies have been made, they have been personally signed and stamped by an educational official who certifies that they are exact copies of the original document. Properly certified and signed copies should be sent instead of irreplaceable original documents. Academic records must be in the language of the institution and should be accompanied by an official English translation. Foreign academic records must show all courses attended each year, examinations passed, seminars completed, and grades or marks received at all institutions where formal records are maintained.

3. Statement of Purpose

The statement of purpose must be submitted on-line through the UCSD Application for Graduate Admission .

An applicant's statement of purpose is very important and is given careful consideration in the selection process. For additional guidelines, please refer to Statement of Purpose . There is no specific word limit, but be concise and specific in preparing your statement, giving information that demonstrates your level of preparation and potential for success in graduate school. Applicants should address past accomplishments within the realm of computer science and engineering, why they are qualified for the program, and goals they wish to pursue while in graduate school.

NOTE: Requests to update the Statement of Purpose are not granted after submission. Please carefully double-check edits edits prior to official submission.

4. Three Letters of Recommendation

Three letters of recommendation are required.

LORs are submitted via the online application only. For additional information about the procedures and policies for letters of recommendation, please review the UCSD Graduate Division guidelines at Procedures for Letters of Recommendations

It is important that letters of recommendation be completed by individuals who are in a position to analyze your abilities and academic promise. References generally hold an academic position. Letters from research or project advisors are also acceptable, as well as LORs from a professional/industry referee.

5. GRE Score Report

The General Test of the Graduate Record Examinations is required of ALL applicants.

The GRE score (self-reported) including test date, results, and registration # must be entered in the online application by the application deadline. An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program. Applicants are advised to schedule their GRE test far enough in advance to meet the deadline.

Applicants should request that ETS submit the scores directly to the UCSD institution code 4836. When ordering your GRE score reports, use UCSD's institution code 4836, the department codes are not necessary. Information about the GRE is available from the Educational Testing Service (ETS) website. Note: An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program.

6. TOEFL Score Report (International Applicants Only)

The Test of English as a Foreign Language (TOEFL) is required for international applicants whose native language is not English and who have not studied full-time at a university-level institution in an English-speaking country for one uninterrupted academic year. ***No other test of English may be used to substitute for TOEFL for the CSE Graduate Application.

The UCSD TOEFL Institution Code is 4836 The university minimum TOEFL score required for admission is 550 for the paper-and-pencil version, 213 for the computer-based test or 80 for the internet-based test (iBT).

For more information on the TOEFL, visit the TOEFL website . The Test of Spoken English (TSE) is not required. An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program.

7. Resume/CV:

A resume or curriculum vita should be uploaded to the online application under the Resume/CV section.

Requests to update Resumes and/or the Statement of Purpose are not granted after submission. Please carefully double-check edits edits prior to official submission.

截止申请时间:

December 18

专业介绍

CSE's master degree programs are designed to address a variety of post-graduate educational needs. As with our PhD programs, we offer majors in both computer science and computer engineering.

Both majors are available in each of our three MS plans: Thesis Plan, Comprehensive Standard Plan, or Comprehensive Interdisciplinary Plan. Course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools, (2) advanced, up-to-date views in topics outside their area (the Breadth requirement), and (3) a deep, current view of their research or specialization are (the Depth requirement). The Interdisciplinary Option requires additional coursework in another department. Courses may not fulfill more than one requirement.

These programs can be completed full-time or part-time by students working in industry.

Applications for admission to the MS program are considered annually. Admissions are effective the following Fall quarter.

Excellent students who develop an interest in pursuing a PhD in the course of their MS studies are encouraged to apply to the PhD program.

联系方式:

If you still have questions for which you could not find an answer on our web site, please email either one of the following:

PhD Admissions at csegradinfo-phd@eng.ucsd.edu

MS Admissions at csegradinfo-ms@eng.ucsd.edu

课程设置

  • 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.

  • The masters degree is offered with the title Computer Science and Engineering or Computer Science and Engineering (Computer Engineering).

  • 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.

  • M.S. Plan I - Thesis

  • M.S. Plan II- Comprehensive Exam, Standard Option

  • M.S. Plan II - Comprehensive Exam, Interdisciplinary Option

  • CSE 200. Computability and Complexity (4)

  • 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.

  • CSE 201A. Advanced Complexity (4)

  • 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.

  • CSE 202. Algorithm Design and Analysis (4)

  • 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.

  • CSE 203A. Advanced Algorithms (4)

  • 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.

  • CSE 205A. Logic in Computer Science (4)

  • (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.

  • CSE 206A. Lattice Algorithms and Applications (4)

  • (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.

  • CSE 207. Modern Cryptography (4)

  • 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.

  • CSE 208. Advanced Cryptography (4)

  • 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.

  • CSE 209A. Topics/Seminar in Algorithms, Complexity, and Logic (1–4)

  • 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.

  • CSE 209B. Topics/Seminar in Cryptography (1–4)

  • 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.

  • CSE 210. Principles of Software Engineering (4)

  • (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.

  • CSE 211. Software Testing and Analysis (4)

  • 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.

  • CSE 216. Research Topics in Human-Computer Interaction (4)

  • 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.

  • CSE 218. Advanced Topics in Software Engineering (4)

  • 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.

  • CSE 219. Design at Large (1)

  • 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.

  • CSE 221. Operating Systems (4)

  • 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.

  • CSE 222A. Computer Communication Networks (4)

  • (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.

  • CSE 222B. Internet Algorithmics (4)

  • (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.

  • CSE 223A. Principles of Distributed Computing

  • Former CSE Course is no longer offered.

  • CSE 223B. Distributed Computing and Systems (4)

  • 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.

  • CSE 227. Computer Security (4)

  • 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.

  • CSE 229A. Topics/Seminar in Computer Systems (1–4)

  • 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.

  • CSE 229B. Topics/Seminar in Networks and Communication

  • Former CSE Course is no longer offered.

  • CSE 229C. Topics/Seminar in Computer Security (1–4)

  • 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.

  • CSE 230. Principles of Programming Languages (4)

  • (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.

  • CSE 231. Advanced Compiler Design (4)

  • (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.

  • CSE 232. Principles of Database Systems (4)

  • (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.

  • CSE 232B. Database System Implementation (4)

  • A hands-on approach to the principles of databases implementation. Algebraic rewriters/optimizers, query processors, triggers. Beyond centralized relational databases. Prerequisites: CSE 232.

  • CSE 233. Database Theory (4)

  • 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.

  • CSE 237A. Introduction to Embedded Computing (4)

  • 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.

  • CSE 237B. Software for Embedded Systems (4)

  • 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.

  • CSE 237C. Validation and Testing of Embedded Systems (4)

  • 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.

  • CSE 237D. Design Automation and Prototyping for Embedded Systems (4)

  • 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.

  • CSE 239A. Topics/Seminar in Databases (1–4)

  • 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.

  • CSE 240A. Principles of Computer Architecture (4)

  • (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.

  • CSE 240B. Parallel Computer Architecture (4)

  • 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.

  • CSE 240C. Advanced Microarchitecture (4)

  • 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.

  • CSE 240D. Application Specific Processors

  • Former CSE Course is no longer offered.

  • CSE 241A/ECE 260B. VLSI Integration of Computing Circuitry (4)

  • 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.

  • CSE 242A. Integrated Circuit Layout Automation

  • Former CSE Course is no longer offered.

  • CSE 243A. Introduction to Synthesis Methodologies in VLSI CAD (4)

  • 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.

  • CSE 244A. VLSI Test (4)

  • 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.

  • CSE 245. Computer Aided Circuit Simulation and Verification (4)

  • 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.

  • CSE 247. Application Specific and Reconfigurable Computer Architecture

  • Former CSE Course is no longer offered.

  • CSE 248. Algorithmic and Optimization Foundations for VLSI CAD (4)

  • 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.

  • CSE 249A. Topics/Seminar in Computer Architecture (1–4)

  • 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.

  • CSE 249B. Topics/Seminar in VLSI (1–4)

  • 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.

  • CSE 249C. Topics/Seminar in CAD

  • Former CSE Course is no longer offered.

  • CSE 250A. Principles of Artificial Intelligence: Probabilistic Reasoning and Learning (4)

  • 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.

  • CSE 250B. Principles of Artificial Intelligence: Learning Algorithms (4)

  • 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.

  • CSE 250C. Machine Learning Theory (4)

  • 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.

  • CSE 252A. Computer Vision I (4)

  • 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.

  • CSE 252B. Computer Vision II (4)

  • 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.

  • CSE 252C. Selected Topics in Vision and Learning (1–4)

  • 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.

  • CSE253 - Neural Networks/Pattern Recognition

  • 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.

  • CSE 254. Statistical Learning (4)

  • 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.

  • CSE 255. Data Mining and Predictive Analytics (4)

  • 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.

  • CSE 256/LING 256. Statistical Natural Language Processing

  • Former CSE Course is no longer offered.

  • CSE258 - Recommender Systems & Web Mining

  • 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.

  • CSE 258A. Cognitive Modeling (4)

  • 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.

  • CSE 259. Seminar in Artificial Intelligence (1)

  • 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.

  • CSE 259C. Topics/Seminar in Machine Learning

  • Former CSE Course is no longer offered.

  • CSE 260. Parallel Computation (4)

  • (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.

  • CSE 262. System Support for Applications of Parallel Computation (4)

  • 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.

  • CSE 272. Advanced Image Synthesis (4)

  • Computer graphics techniques for creating realistic images. Topics include ray tracing, global illumination, subsurface scattering, and participating media. CSE 168 or equivalent recommended.

  • CSE 274. Selected Topics in Graphics (2–4)

  • 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.

  • CSE 280A. Algorithms in Computational Biology (4)

  • (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.

  • CSE 282/BENG 202. Bioinformatics II: Sequence and Structure Analysis—Methods and Applications (4)

  • (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.

  • CSE 283/BENG 203. Bioinformatics III: Functional Genomics (4)

  • Annotating genomes, characterizing functional genes, profiling, reconstructing pathways. Prerequisites: Pharm 201, BENG 202/CSE 282, or consent of instructor.

  • CSE 290. Seminar in Computer Science and Engineering (1–4)

  • (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.)

  • CSE 291. Topics in Computer Science and Engineering (1–4)

  • 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.)

  • CSE 292. Faculty Research Seminar (1)

  • (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.

  • CSE 293. Special Project in Computer Science and Engineering (1–12)

  • 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.)

  • CSE 294. Research Meeting in CSE (2)

  • 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.

  • CSE 298. Independent Study (1–16)

  • 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.

  • CSE 299. Research (1–16)

  • Research. Prerequisites: consent of faculty.

  • CSE 500. Teaching Assistantship (2–4)

  • 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.

  • CSE 599. Teaching Methods in Computer Science (2)

  • 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.

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