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.
T. Dreyfus, C. Rasmussen, N. Apkarian, и M. Tabach. INDRUM 2018, (2018)"The complexity of knowledge flow in the classroom, even based on this one class session, is far greater than one might imagine. ".
D. Spikol, и J. Eliasson. The 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, стр. 137--141. IEEE, (2010)