The course will focus on theory and algorithms for mathematical optimization and their applications in modern machine learning. We will touch upon formal models and algorithms for online learning and sequential decision making under uncertainty, convex optimization, stochastic optimization, statistical learning and generalization, and non-convex optimization. Special attention will be given to the computational efficiency of algorithms, which is critical to modern applications.

Prerequisites: Introduction to Machine Learning, general mathematical maturity (calculus, linear algebra, probability theory).