Course Announcement for Spring 2007
CSCE 970: Pattern Recognition
(with an emphasis on graphical models)

Instructor:

Stephen Scott
Avery 364
sscott AT cse
http://www.cse.unl.edu/~sscott/
Previous offering: http://cse.unl.edu/~sscott/teach/Classes/cse970S03/

Meeting Time: 10:30-11:20 Mon/Wed/Fri in Avery 112 [subject to change]

Track Classification: This course applies towards Applications track requirements for all graduate programs in Computer Science.

Credits: 3 units

Prerequisites: Math 314 (Linear Algebra), CSCE 310 (Data Structures & Algorithms), STAT 380 (Prob. and Stats.)

Required Textbook: Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer, 2006. ISBN 0-387-31073-8

Optional Textbook: Learning Bayesian Networks by Richard E. Neapolitan, Prentice Hall, 2004. ISBN 0-13-012534-2

Course Description:

Pattern recognition is concerned with the question of how to automatically detect subtle patterns in data. Pattern recognition has for many years been applied to numerous areas, including computer vision, signal processing, bioinformatics, data mining, and many more.

In this course you will learn several of the fundamentals and current trends in pattern recognition, with an emphasis on so-called "graphical models" such as Bayesian networks and hidden Markov models. These models are rigorously justified, provide a distributed knowledge representation, and are as understandable as a rule base. They deal particularly well with uncertainty, and they can be manually generated by consultation of an expert, or inductively built by machine learning. These approaches are used for modelling knowledge in gene regulatory networks, medicine, engineering, text analysis, image processing, data fusion, and decision support systems. A sampling of specific applications includes (1) automatic interpolation of raw data from interplanetary probes and deep space explorations; (2) modeling pilot-aircraft interactions; (3) the "Office Assistant" that was introduced in the Office 95 suite of desktop products; and (4) modeling of DNA and protein sequences.

In contrast with previous offerings of this course, there will be no homeworks or project. Instead, the course requirements will be regular attendance, a major written and oral presentation of course material, substantive edits of other students' written presentations of course material, and at the end of the term a presentation of your own research, tied as much as possible to the course content. For information on the previous offering of this course, see http://cse.unl.edu/~sscott/teach/Classes/cse970S03



Last modified 16 August 2011; please report problems to sscott AT cse.