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.
What is modeling instruction? Instead of relying on lectures and textbooks, the Modeling Instruction program emphasizes active student construction of conceptual and mathematical models in an interactive learning community. Students are engaged with simple scenarios to learn to model the physical world. Find a Modeling Workshop in your area this summer! Modeling Instruction in the…
While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate. In a recent article, I discussed why the broader artificial intelligence field is…
K. Kawaguchi, L. Kaelbling, und Y. Bengio. (2017)cite arxiv:1710.05468Comment: To appear in Mathematics of Deep Learning, Cambridge University Press. All previous results remain unchanged.