Catalog Description: 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.

Units: 4

Prerequisites: Graduate-level mathematical maturity, including proof-based graduate-level courses in at least two, but recommended three, of the following categories: Statistics and Probability, e.g., STAT205A, STAT210B Economics, e.g., ECON207A Algorithms, e.g., CS270 Optimization, e.g., EE 227B Control theory, e.g., EE 221A

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.

Formats:
Fall: 3.0 hours of lecture per week
Spring: 3.0 hours of lecture per week

Grading basis: letter

Final exam status: No final exam

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