Theoretical Machine Learning

Can the mechanism of learning be automated and implemented by a machine? In this course we formally define and study various models that have been proposed for learning. The course presents and contrasts the statistical, computational and game-theoretic models for learning. Likely topics: intro to statistical learning theory and generalization; learning in adversarial settings on-line learning; analysis of convex and nonconvex optimization algorithms, using convex optimization to model and solve learning problems; learning with partial observability; boosting; reinforcement learning and control; introduction to theory of deep learning.