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.
S. Chen, E. Dobriban, и J. Lee. (2019)cite arxiv:1907.10905Comment: Changed title. Added results on overparametrized 2-layer nets. Added error bars to experiments. Numerous other minor improvements.