CS 598

Spring 2009 Part of Term 1

Part of Term 1
Jan 20-May 6

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 2009
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
50226	Lecture-Discussion	CSC	12:30PM -1:45PM	WF	1302 Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 01/20/09-05/06/09 Credit: 4 hours Section Info: Topic: Approximation Algorithms. Approximation algorithms for NP-hard problems are polynomial time heuristics that have provably good guarantees on the quality of their solutions. This course will provide a broad introduction to results and techniques in this area. Emphasis will be on fundamental problems and techniques that are of wide applicability. In particular, this terms class will focus mostly on linear and semi-definite programming methods for graph problems.
46411	Lecture-Discussion	DWH	11:00AM -12:15PM	TR	1109 Siebel Center for Comp Sci	Hoiem, D	Part of Term: 1 Date Range: 01/20/09-05/06/09 Credit: 4 hours Section Info: Topic: Visual Scene Understanding. As humans, we have an amazing ability to interpret images and video, and we would like to give computers this same capacity to describe the setting, objects, actors, and their activities. Such scene understanding requires not only identifying different components of the scene, but also capturing the interplay among them. This course will focus on contextual and integrative methods in computer vision, covering object recognition, segmentation, scene classification, activity recognition, and other scene analysis algorithms. Material will be presented through lecture and paper presentation and discussion, and students will perform a research project. As prerequisites, students should be familiar with basic techniques in image analysis and machine learning.
31662	Lecture-Discussion	EA	2:00PM -3:15PM	TR	1103 Siebel Center for Comp Sci	Amir, E	Part of Term: 1 Date Range: 01/20/09-05/06/09 Credit: 4 hours Section Info: Topic: Logic-Based Artificial Intelligence. This class will cover techniques, formulations, and problems in knowledge representation and logical AI. Among them we will consider representing knowledge about time, space, entities, relationships, default knowledge and inference, beliefs and beliefs over others beliefs, and semantic information. Tools that we will examine will include mainly logics: Description logics, Modal logics, First-Order logic, Set Theory, and ad-hoc formulations and languages. The class will be in the format of paper presentation by students and individual or group research-level projects. More details are available at the class website: http://reason.cs.uiuc.edu/eyal/classes/sp09/cs598ea
50225	Lecture-Discussion	JHM	12:30PM -1:45PM	WF	1103 Siebel Center for Comp Sci	Hockenmaier, J	Part of Term: 1 Date Range: 01/20/09-05/06/09 Credit: 4 hours Section Info: Topic: Advanced Natural Language Processing: Algorithms and models for grammar induction, parsing and machine translation. The first part of the course will give an overview of the grammar formalisms, statistical models, and search algorithms used in natural language parsing and grammar induction. The second part of the course will show how many of these ideas can be extended and applied to machine translation. We will also look at ways to implement and train very large-scale NLP systems (using MapReduce/Hadoop, Bloom filters, etc.) The course will consist of a mixture of lectures and seminar-style presentations done by students. Prerequisites: Machine learning (CS446), prior exposure to NLP (one of CS498, LING406, CS546) or approval of the instructor.
31665	Lecture-Discussion	REJ	3:00PM -4:45PM	TR	1304 Siebel Center for Comp Sci	Johnson, R	Part of Term: 1 Date Range: 01/20/09-05/06/09 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.
43781	Lecture-Discussion	SS	2:00PM -3:15PM	TR	1131 Siebel Center for Comp Sci	Sinha, S	Part of Term: 1 Date Range: 01/20/09-05/06/09 Credit: 4 hours Section Info: Topic: Probabilistic Methods for Biological Sequence Analysis. This is an advanced topics course in bioinformatics. We will discuss (i) probabilistic techniques such as Expectation-Maximization, Hidden Markov Models, Bayesian inference, Monte carlo sampling (ii) computational assessment of sequence statistics (such as alignment scores and word frequencies), (iii) mathematical models of evolution and their use in sequence analysis, among other topics. Computational techniques will be discussed in the context of the important biological process of gene regulation, and problems such as "sequence alignment", "motif finding", and "module detection", will be studied in detail. NO BACKGROUND IN BIOLOGY IS REQUIRED: biological concepts used will be introduced early in the course. The course will involve a research project. Prerequisites: Programming, basic probability and statistics.