CMPS 242: Machine Learning
- Fall 2009
- Winter 2009
- Winter 2008
- Fall 2006
- Fall 2005
- Winter 2005
- Fall 2003
- Fall 2002
- Fall 2001
- Spring 2001
- Spring 2000
Introduction to machine learning algorithms. Covers learning models from fields of statistical decision theory and pattern recognition, artificial intelligence, and theoretical computer science. Topics include classification learning and the Probably Approximately Correct (PAC) learning framework, density estimation and Bayesian learning, EM, regression, and online learning. Provides an introduction to standard learning methods such as neural networks, decision trees, boosting, nearest neighbor, and support vector machines. Requirements include one major experimental learning project or theoretical paper. Enrollment restricted to graduate students. Enrollment limited to 30. D. Helmbold, M. Warmuth
5 Credits
While the information on this web site is usually the most up to date, in the event of a discrepancy, please contact your adviser to confirm which information is correct.