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Additional Course Descriptions 2009-2010

CS 525D Knowledge Discovery and Data Mining (Fall 09)

Instructor: C. Ruiz

Due to advances in technology and the availability of increasingly cheap storage devices, data in different domains have been accumulating at an impressively high rate in recent years, leading to very large databases. This course presents current research in Knowledge Discovery in Databases (KDD) dealing with the data integration, mining, and interpretation of patterns in such databases. Topics include data warehousing and mediation techniques aimed at integrating distributed, heterogeneous datasources; data mining techniques such as rule-based learning, decision trees, association rule mining, and statistical analysis for discovery of patterns in the integrated data; and evaluation and interpretation of the mined patterns using visualization techniques. The work discussed originates in the fields of databases, artificial intelligence, information retrieval, data visualization, and statistics. Industrial and scientific applications will be given. (Prerequisites: Background in databases, artificial intelligence, and statistics at the undergraduate level, or permission of the instructor. Proficiency in a high level programming language, preferable Java, is required.)

CS 525? Graphical Models for Reasoning Under Uncertainty (Fall 2009)

Instructor: J. Beck

This course will introduce students to graphical models, such as Bayesian networks, Hidden Markov Models, Kalman filters, particle filters, and structural equation models. Graphical models are applicable in a wide variety of work in computer science for reasoning under uncertainty such as: user modeling, speech recognition, computer vision, object tracking, and determining a robot's location.  This course will cover 1) using data to estimate the parameters and structure of a model using techniques such as expectation maximization, 2) techniques for performing efficient inference, such as junction trees and sampling, on new observations, and 3) evaluation techniques to determine whether a particular model is a good one.

CS525C / ECE 5311. Information Theory and Coding (Fall 2009)

Instructor: A. Klein

This course introduces the fundamentals of information theory and discusses applications in compression and transmission of data. Measures of information, including entropy, and their properties are derived. The limits of lossless data compression are derived and practical coding schemes approaching the theoretical limits are presented. Lossy data compression tradeoffs are discussed in terms of the rate-distortion framework. The concept of reliable communication through noisy channels (channel capacity) is developed. Techniques for practical channel coding, including block and convolutional codes, are also covered. (Prerequisite: background in probability and random processes such as in ECE502 or equivalent).

CS 525A Computer Animation (Spring 2010)

Instructor: M. Ward

This course expands on the material covered in CS 543 (Computer Graphics) by examining algorithms, data structures, and techniques used in modeling and rendering dynamic scenes. Topics include an overview of traditional animation, animation hardware and software, parametric blending techniques, modeling physical and articulated objects, forward and inverse kinematics, key-frame, procedural, and behavioral animation, and free-form deformation. Students will be expected to develop programs to implement low-level algorithms commonly found in animation packages as well as use commercial animation tools to design and produce small to moderate sized animations. Students will also be expected to read journal and conference papers in the area of computer animation (from a technical, rather than artistic, stand-point) and make presentations.

CS 525T Intelligent Tutoring Systems (Spring 2010)

Instructor: N. Heffernan

(Draft Description) This course addresses the use of artificial intelligence and cognitive psychology to build computer-based "intelligent tutoring systems". Students will learn empirical and theoretical methods for creating cognitive models of human problem solving. Such models have been used to create educational software that has been demonstrated to dramatically enhance student learning in domains like mathematics and computer programming. This course will have three components; 1) literature review of some of the fundamental papers in the field, 2) some lectures on the needed cognitive psychology and human-computer interaction (HCI) background, and 3) a significant project component in which students will be practicing the use of methods used to design tutors. Students will create cognitive models written in a production-rule system (e.g., JESS). The culminating project will be building a cognitive model to be used in an intelligent tutoring system for a domain of their interest. At the end of this course a student should be able to do research in intelligent tutoring systems. Good programming skills are required. Artificial intelligence would he helpful but not required. Knowledge of cognitive psychology or human-computer interaction would be a plus. Good class projects will potentially be used by thousands of students using our extensible web-based delivery mechanism.

 

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Last modified: September 07, 2009 16:18:37