Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., that learn to spot high-risk medical patients, recognize speech, classify text documents, detect credit card fraud, or drive autonomous robots).

This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, maximum entropy model, support vector machine and kernel methods, graphic models, Bayesian learning methods, dimension reduction , feature and model selection. This course covers theoretical concepts such as inductive bias, the PAC learning framework, minimum description length principle, and Occam's Razor. We will also provide brief tutorials on Information Theory, optimization theory, and Bayesian Statistics, as needed.

Homework assignments include both theoretic derivation and hands-on experiments with various learning algorithms. Every student is required to finish a project that is either assigned by the intructor or designed by the student himself.

Last modified: 26-Dec-2003 10:02 PM