Theoretical Machine Learning

In this course we formally define and study various models that have been proposed for learning. We will present and rigorously analyze some of the most successful algorithms in machine learning that are extensively used today. Topics include: intro to statistical learning theory and generalization error bounds; learning in adversarial settings and the on-line learning model; mathematical optimization in machine learning; learning with partial observability; reinforcement learning; online control and learning in dynamical systems.