# 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*