Instructor: Yaron Singer
Course description: This is a rigorous graduate-level course on optimization. The course covers convex and combinatorial optimization for solving large-scale problems. In recent years optimization has had a profound impact on machine learning, data analysis, mathematical finance, signal processing, control, theoretical computer science, and many other areas. The first part of the course will be dedicated to the theory of convex optimization and its direct applications. The second part will focus on advanced techniques in combinatorial optimization using machinery developed in the first part. Throughout the course we will see applications such as linear classification, LASSO, boosting, portfolio selection, online learning, neural networks, Support Vector Machines (SVMs), influence in networks, clustering, Principal Component Analysis (PCA) and dimensionality reduction techniques, feature selection, Generative Adversarial Networks (GANs), and adversarial attacks in machine learning.