Statistical Learning Theory

author: Olivier Bousquet, Google, Inc.
published: Feb. 25, 2007,   recorded: August 2003,   views: 31594


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This course will give a detailed introduction to learning theory with a focus on the classification problem. It will be shown how to obtain (pobabilistic) bounds on the generalization error for certain types of algorithms. The main themes will be: * probabilistic inequalities and concentration inequalities * union bounds, chaining * measuring the size of a function class, Vapnik Chervonenkis dimension, shattering dimension and Rademacher averages * classification with real-valued functions  Some knowledge of probability theory would be helpful but not required since the main tools will be introduced.

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Reviews and comments:

Comment1 ibrahim güney, March 21, 2007 at 11:39 a.m.:


Comment2 clueless, December 29, 2007 at 11:49 p.m.:

Doesn't work on Mac!

Comment3 hardlianotion, July 26, 2008 at 6:17 p.m.:

Does work on Mac!

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