Courses (check online schedule for availability)
                     (click on course name for course website)

CS 396 - Introduction to Pattern Recognition

Description: Pattern Recognition techniques are useful in many applications of computer science and information systems, such as information retrieval, data mining, artificial intelligence and image processing. This course is an introduction to the foundation of pattern recognition algorithms.

CS 619 - Data Mining

Description: This course will provide an overview of topics such as introduction to data mining and knowledge discovery; data mining with structured and unstructured data; foundations of pattern clustering; clustering paradigms; clustering for data mining; data mining using neural networks and genetic algorithms; fast discovery of association rules; applications of data mining to pattern classification; and feature selection. The goal of this course is to introduce students to current machine learning and related data mining methods. It is intended to provide enough background to allow students to apply machine learning and data mining techniques to learning problems in a variety of application areas.

CS 627 - Artificial Intelligence

Description: Students will learn a number of search methods, including heuristic search, branch-and-bound, uniform-cost search, and A*. Various methods of knowledge representation are covered, and the student is given a broad knowledge of the fundamentals of a number of topics in AI, including neural networks, genetic algorithms, speech recognition and understanding, computer vision, and robotics.

CS 630 - Intelligent Agents

Description: This course covers the basics of rule-based programming using Soar, including the design of problem spaces, the use of operators to solve problems, and universal subgoaling. Students write Soar agents for a simple Pacman-like video game and a simulated tank, and program basic subgoaling and chunking.

CS 631 - Computer Vision and Pattern Recognition

Description: This course introduces the student to computer vision algorithms, methods and concepts which will enable the student to implement computer vision systems with emphasis on visual pattern recognition. Upon successful completion of this course of study a student will have general knowledge of image analysis and processing, pattern recognition techniques, and some experience with research in computer vision.

727 - Advanced Artificial Intelligence

Description: This course is a masters level course that is crosslisted with 827.

827 - Advanced Artificial Intelligence

Description: This PhD course covers more advanced AI topics, including planning, hidden markov models, markov decision processes, reinforcement learning, and bayesian inference.