Welcome

This site provides information on the CS 4640 course "Machine Learning", to be taught in the Spring 2002 Semester. The site will be updated frequently, as the course moves on. So stay tuned!

Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Occam's Razor. Programming assignments include hands-on experiments with various learning algorithms. Typical assignments include neural network learning for face recognition, and decision tree learning from databases of credit records.

This course is heavily modeled on the Machine Learning course originally taught by Tom Mitchell, and later by Sebastian Thrun, at Carnegie Mellon University.

Frank Dellaert