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.
Today we’re thrilled to release mathsteps — the first open-source project that teaches math step-by-step. Learn about how and why we built it, and join us in making math easy and fun to learn.
The Association exists to bring about improvements in the teaching of mathematics and its applications, and to provide a means of communication among students and teachers of mathematics. Its work is carried out through its Council and committees.