Graduate classes, Fall 2010, Computer Science

CS 524: Theory Of ComputingCredits: 4− Description− Sections
Content: This course gives mathematical methods to classify the complexity of computational problems. Topics include regular languages, grammars, decidability, NP-completeness, and corresponding models of computation.
Texts: Introduction to the Theory of Computation, by Spiser. Course Technology. ISBN 10: 0534950973.
Assessments: TBA
Prerequisites: CS 124 and 253.
000MSC: E408MWF 12:50pm - 1:40pmMichelangelo Grignimax 16
CS 551: Systems ProgrammingCredits: 4− Description− Sections
Content: TBA
Texts: Advanced Unix Programming, by Marc J. Rochkind. Addison Wesley Professional. ISBN# 10-0131411543.
Assessments: TBA
Prerequisites: TBA
000MSC: W301TuTh 2:30pm - 3:45pmKen Mandelbergmax 20
CS 556: Compiler ConstructionCredits: 4− Description− Sections
Content: An introduction to the algorithms and data structures used to construct a high level language compiler. Topics include: formal language specification, lexical analysis, parsing, and code generation.
Texts: Principles, Techniques, and Tools, by Aha, Sethi, Ullman, & Lam. Pearson Education, Inc. ISBN 0-471-69466-5.
Assessments: TBA
Prerequisites: CS 253, CS 255, and CS 424, or their equivalents. A substantial portion of this course will involve a student project to construct a compiler for a simplified programming language. Working knowledge of C or C++ is highly recommended.
000MSC: W301MW 3:00pm - 4:15pmJames Lumax 10
CS 558: NetworkingCredits: 4− Description− Sections
Content: This course studies performance issues in computer networks. The course starts with a brief introduction to Markov Analysis. The Markov chain analysis technique is then used to study the performance of classic protocols such as Aloha, CSMA and new wireless protocols, such as 802.11. The second topic is congestion control in the Internet. The performance of the TCP protocol will be studied analytically as well as empirically using the NS2 network simulator. A number of TCP performance issues will also be discussed, including TCP synchronization, High speed TCP and RTT unfairness. The last portion of the course will study advanced network techniques to ensure quality of service (QoS). Topics include fairness guarantee, delay guarantee, and deadline guarantee. If time permits, we will also study multicasting, video on demand and other exotic network protocols.
Texts: no text book (online notes and research papers)
Assessments: homeworks, one midterm and one final
Prerequisites: CS458
000MSC: W304TuTh 1:00pm - 2:15pmShun Yan Cheungmax 25
CS 584: Topics in Computer Science: Software EngineeringCredits: 4− Description− Sections
Content: This course seeks to teach principles of software engineering through hands-on experience in a series of projects, and through a survey of the skills and body of knowledge software engineers are expected to possess. These include Requirements, Design, Construction, Testing, Quality Control, and Configuration Management. In addition, the student will be introduced to applied concepts in software engineering through the blogs and writings of some of today's most noted practitioners.
Texts: Software Engineering: A Practitioner’s Approach (Roger S. Pressman, 7th edition) will serve as the primary text. Other materials used in the course are available on the web.
Assessments: A combination of exams, homework and project work will provide the basis for assessments.
Prerequisites: Graduate student, or Senior standing, with permission from the instructor. The student must be competent in at least one language used in web software development.
001MSC: W304TuTh 4:00pm - 5:15pmJoan Smithmax 10
CS 584: Topics in Computer Science: Machine LearningCredits: 4− Description− Sections
Content: This course will cover the theory and practice of machine learning. We will discuss both supervised approaches (linear methods for classification, rule based methods, kernels, prototype based methods) and unsupervised approaches (clustering, linear and non-linear dimension reduction). Student presentations on topics from the primary text, research papers, and applied projects will make up a significant portion of the course
Texts: Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. **Second Edition** The text is available online at http://www-stat.stanford.edu/~tibs/ElemStatLearn/download.html, but a physical copy is **highly** recommended.
Assessments: TBA
Prerequisites: CS377
000MSC: E406TuTh 11:30am - 12:45pmJames Taylormax 16
CS 590: Teaching SeminarCredits: 1− Description− Sections
Content: This seminar will concentrate on effective teaching techniques in computer science. Topics included will include: General advice for new TA's. General advice for International TA's. Students will present several practice lectures over different levels of material. They will receive practice on quiz and test preparation. Syllabus information on courses most likely to be taught by new TA's will be supplied. General professional development information will also be included.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
000MSC: E408F 2:00pm - 2:50pmShun Yan Cheungmax 999
CS 597R: Directed StudyCredits: 1 - 12− Description− Sections
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
00PTBAFaculty (TBA)max 999
CS 598R: Rotation ProjectCredits: 1 - 4− Description− Sections
Content: Computer Science and Informatics PhD students are required to complete two rotation projects prior to their qualifying exams and dissertation research. Projects often involve interdisciplinary work, and can be co-supervised by a Math/CS faculty and an external faculty member or researcher (e.g., Schools of Medicine and Public Health, the CDC). Students are required to submit a project proposal and a final report.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
000TBAFaculty (TBA)max 999
01PEugene Agichtein
02PShun Yan Cheung
03PMichelangelo Grignimax 999
04PJames Lumax 999
05PKen Mandelberg
06PJames Nagymax 999
07PVaidy Sunderammax 999
08PJames Taylor
09PJoan Smith
CS 599R: Master's Thesis ResearchCredits: 1 - 12− Description− Sections
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
000PTBAFaculty (TBA)max 999
CS 700R: Graduate SeminarCredits: 1− Description− Sections
Content: This is a required course for all students in the PhD program. It comprises seminars given by faculty, invited guests, and students.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
000MSC: W301F 3:00pm - 4:15pmJames Lumax 25
CS 730R: Topics in Data and Information ManagementCredits: 4− Description− Sections
Content: This is an advanced graduate course in the focus area of the Computer Science and Informatics PhD program. Coverage and syllabus will vary according to the instructor and emphasis will be on current developments in the field.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
000Emerson E510M 10:00am - 12:30pmEugene Agichteinmax 16
CS 797R: Directed StudyCredits: 1 - 12− Description− Sections
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
00PTBAFaculty (TBA)max 999
CS 799R: Dissertation ResearchCredits: 1 - 12− Description− Sections
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
00PTBAFaculty (TBA)max 999