CS 795/895 Machine Learning

Fall 2010

Old Dominion University

This course will investigate the area of machine learning, including Bayesian techniques, Linear Discrimination, Perceptrons, and Hidden Markov Models. Practical issues of model construction and conditioning will be considered.

Students in the class will be expected to design an experiment comparing the effectiveness of two or more different techniques for problem sets provided by the instructor or arising out of outher departmental research projects, to carry out the experiemnt, and to prepare and present a paper based upon their findings.

Offered: MW 9:30-10:45, ECSB 2120

Text: Ethem Alpaydin, Introduction to Machine Learning, 2004, The MIT Press, ISBN 0-262-01211-1

Prerequisites: graduate standing in CS and commensurate maturity in mathematics, statistics, and programming.