Spring 2022 STAT 37789

Course: STAT 37789

Title: Topics in Machine Learning: Learning in Games

Instructor(s): Alexander Strang

Teaching Assistant(s):

Class Schedule: Sec 1: TR 9:30 AM-10:50 AM in Jones 226

Description: Games have long been used as benchmarks in artificial intelligence, and research in game playing has closely tracked major developments in computing. Famous examples include IBM's Deep Blue and Google Deepmind's AlphaGo. Driven by advances in machine learning, recent years have seen rapid progress in the field of game playing artificial intelligences. This reading course will review the major achievements in learning in games, discuss different classes of games, and the algorithms used to select good strategies. We will introduce relevant game theory, discuss classical methods for complete information games and combinatorial games, and modern learning methods such as counterfactual regret minimization methods used in incomplete information games. We will conclude by identifying the frontiers of artificial intelligence in games. Students are expected to enter with rudimentary coding experience.