This course is an advanced topics course intended for graduate students with a background in optimization, linear systems theory, probability and statistics, and an interest in learning, game theory, and decision making more broadly. We will cover the basics of game theory including equilibrium notions and efficiency, learning algorithms for equilibrium seeking, and discuss connections to optimization, machine learning, and decision theory. While there will be some initial overview of game theory, the focus of the course will be on modern topics in learning as applied to games in both cooperative and non-cooperative settings. We will also discuss games of partial information and stochastic games as well as hierarchical decision-making problems (e.g., incentive and information design). Not offered 2024-25.