CMPS242: Machine Learning

*****COURSES ARE SUBJECT TO CHANGE*****

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

YearFallWinterSpringSummer
2017-18
2016-17
2015-16
2014-15
2013-14
2012-13
2011-12
2010-11
2009-10
2008-09
  • Section 01
    David Helmbold (dph)
    Telecast to Los Alamos & SVC
2007-08
2006-07
2005-06
2004-05
2003-04
2002-03
2001-02
2000-01
1999-00

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.