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. Yagli, A. Dytso, и H. Poor. (2020)cite arxiv:2005.02503Comment: Accepted for publication in Proceedings of 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020. arXiv version is 10pt font, 6 Pages. This is the same document as the SPAWC version, except that the conference version is written with 9pt font to meet the strict page margin requirements.