Statistical Learning Theory

Posted in Science on September 12, 2008


Statistical Learning Theory

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.

Author: Olivier Bousquet, Google

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Statistical Learning