Welcome

This site provides information on the CS 4641/7641 course "Machine Learning", to be taught in the Spring 2003 Semester. The site will be updated frequently, as the course moves on. So stay tuned!

There is also a newsgroup git.cc.class.csx641 where assignments, exercises, and other general news will be posted, and that can be used to communicate about the class/assignments.

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, Bayesian learning methods, graphical models, hidden Markov models, and reinforcement learning. The course covers theoretical concepts such as inductive bias and Occam's Razor, the PAC learning framework, and bagging/boosting.  Programming assignments include hands-on experiments with various learning algorithms, and applications of machine learning in bio-informatics, data-mining, robotics, and computer vision will be explored.

Frank Dellaert

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