This course introduces students to the mathematical foundation of learning in the presence of strategic and societal agency. This is a theory-oriented course that will draw from the statistical and computational foundations of machine learning, computer science, and economics. As a research-oriented course, a range of advanced topics will be explored to paint a comprehensive picture of classical and modern approaches to learning for the purpose of decision making.These topics include foundations of learning, foundations of algorithmic game theory, cooperative and non-cooperative games, equilibria and dynamics, learning in games, information asymmetries, mechanism design, and learning with incentives.

* Time conflicts are NOT allowed

Requisites

• Graduate students NOT in the Master of Engineering Program other those in EECS

Credit Restrictions

Students will receive no credit for COMPSCI 272 after completing COMPSCI 272. A deficient grade in COMPSCI 272 may be removed by taking COMPSCI 272.

Repeat Rules

Course is not repeatable for credit.