CS 598

Spring 2010 Part of Term 1

Part of Term 1
Jan 19-May 5

Special Topics

Credit: 2 TO 4 hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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CS 598 class schedule data for spring 2010
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
50226	Lecture-Discussion	CSC	11:00AM -12:15PM	TR	Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topics: Combinatorial Optimization. This course will cover some advanced topics such as non-bipartite matchings, ellipsoid method and the polyhedral approach to combinatorial optimization, matroids, submodularity, multicommodity flows, graph connectivity and orientations. Emphasis is on structural results and good characterizations via min-max results and less on efficiency of resulting algorithms. Prerequisites: graduate algorithms and/or basics of network flows and linear programming.
39668	Lecture-Discussion	DAF	3:30PM -4:50PM	TR	Siebel Center for Comp Sci	Forsyth, D	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Energy Methods in Applied Computer Science.
50225	Lecture-Discussion	JHM	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Hockenmaier, J	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Advanced NLP: Theory and applications of Bayesian models. In recent years, Bayesian techniques have been applied to a number of natural language processing tasks. The aim of this course is to provide students with an understanding of the theory behind these models, and to enable them to apply these techniques in their own research. We will study Bayesian models such as Latent Dirichlet Allocation (topic models) and (Hierarchical) Dirichlet Processes and their applications to various natural language processing tasks. We will review both variational and sampling-based inference algorithms. The course will consist of a research project and a mixture of lectures and seminar-style presentations. Prerequisites: Machine learning (CS446), prior exposure to NLP (one of CS498, LING406, CS546) or approval of the instructor.
46428	Lecture-Discussion	LRS	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Sha, L	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Cyber Physical Systems. The convergence of computing and networking gives us the Internet, and the coming convergence of the Cyber world and the sensing and control of physical environment gives us CPS. The President's Council of Advisors on Science and Technology has placed CPS on the top of the priority list for federal research investment, so are EU. This class will introduce you to the technologies to address the key challenge of achieving simplicity, safety and effectiveness when we composing CPS systems using components with different degree of reliability. In addition to lectures, there is a hands-on project. This year's project is on the topic of safe medical device composition. To learn more about this challenge, see http://mdpnp.org/uploads/Capitol_Hill_NSF_CPS_MD_PnP_9July09.pdf . This class admits both senior undergrads and graduate students.
31665	Lecture-Discussion	REJ	3:00PM -4:45PM	TR	Siebel Center for Comp Sci	Johnson, R	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Object-Oriented Programming and Design. Learn object-oriented design by studying examples from Squeak, many of which have been polished for 25 years. Learn about design patterns, how to use frameworks and how to design them, and reflection. Prerequisite: Graduate standing or Consent of Instructor.
31663	Lecture-Discussion	SHP	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Har-Peled, S	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Geometric Approximation Algorithms. In this course we will survey basic geometric approximation algorithms. Topics covered include: random sampling, discrepancy, embeddings, convex shape approximation, dimension reduction, shape fitting, fast clustering in low dimensions, and other topics.
39664	Lecture-Discussion	YZY	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Yu, Y	Part of Term: 1 Date Range: 01/19/10-05/05/10 Credit: 4 hours Section Info: Topic: Information Visualization. Information visualization helps people understand and analyze data. In contrast with scientific visualization, information visualization focuses on abstract data sets, such as unstructured text, graphs or points in a high-dimensional space. This course teaches principles as well as existing techniques, systems and evaluation methods in information visualization.