CMPS242: Machine Learning

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. Students may not receive credit for both this course and course 142. Enrollment restricted to graduate students, and to: CMPSMS, CMPSPHD, CMPEMS, CMPEPHD, AMSPHD, BMEPHD, EEPHD, TIMPHD

5 credits

Year	Fall	Winter	Spring	Summer

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