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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 inequ ...

This set of lectures will complement the statistical learning theory course and focus on recent advances in the domain of classification. 1- PAC Bayesian bounds: a simple derivation, comparison with Rademacher averages. \\ 2 - Local Rademacher complexity with classification loss, Talagrand's inequal ...

The goal of this course is to introduce the key concepts of learning theory. It will not be restricted to Statistical Learning Theory but will mainly focus on statistical aspects. Instead of giving detailed proofs and precise statements, this course will aim at providing some useful conceptual tools ...

- Learning Theory: Foundations and Goals \\ - Learning Bounds: Ingredients and Results \\ - Implications: What to conclude from bounds \\