Computer Science 281A

Title Statistical Learning Theory
Units 3
Prerequisites Linear algebra, calculus, basic probability, and statistics, algorithms. Recommended 289.
Description Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Also listed as Statistics C241A.
Sections Instructor Teaching Effectiveness How worthwhile was this course?
Fall 2015 Peter Bartlett 5.8 / 7 5.7 / 7
Fall 2014 Ben Recht 5.5 / 7 5.8 / 7
Spring 2014 Michael Jordan 6.4 / 7 6.2 / 7
Fall 2012 Martin J. Wainwright 6.3 / 7 6.2 / 7
Fall 2011 Michael Jordan 6.0 / 7 6.3 / 7
Martin J. Wainwright 5.9 / 7 6.2 / 7
Fall 2010 Michael Jordan 5.6 / 7 6.1 / 7
Martin J. Wainwright 6.3 / 7 6.1 / 7
Fall 2009 Peter Bartlett 4.4 / 7 5.3 / 7
Fall 2008 Martin J. Wainwright 6.1 / 7 6.0 / 7
Fall 2007 Michael Jordan 6.1 / 7 6.2 / 7
Fall 2005 Martin J. Wainwright 6.0 / 7 5.8 / 7
Fall 2004 Michael Jordan 6.1 / 7 6.0 / 7
Fall 2003 Peter Bartlett 5.4 / 7 6.0 / 7
Fall 2002 Michael Jordan 6.0 / 7 6.1 / 7
Overall Rating Teaching Effectiveness How worthwhile was this course?
5.8 / 7 6.0 / 7
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