Course Information
- Time: Monday & Wednesday 11:00AM-12:15PM
- Location: Rice Hall 032
- Instructor: Chen-Yu Wei
Overview
This course studies how algorithms learn from repeated interaction with possibly changing or strategic environments, and their applications in optimization, games, forecasting, and mechanism design. We begin with simple online learning problems, and gradually build toward applications in equilibrium computation, calibrated forecasting, fairness auditing, downstream decision-making, strategic manipulation, algorithmic pricing, and Bayesian persuasion. The goal is to understand how to design learning algorithms with guarantees of stability, reliability, and incentive compatibility.
Prerequisites
Probability, linear algebra, calculus, convex analysis, mathematical maturity.
Grading (tentative)
- (60%) Assignments: 4-5 mathematical problem sets
- (30%) Paper presentation
- (10%) Participation