Course Summary
This is a graduate-level course full-lecture course. It covers several foundational topics in algorithmic game theory and explores how these ideas inspire and connect with modern research in machine learning and AI. Topics include Nash equilibrium, social choice theory, no-regret online learning in game theory, multi-agent reinforcement learning.
This course is primarily based on materials from two outstanding courses by Prof. Ariel Procaccia (link) and Prof. Aaron Roth (link), used with their kind permission.
Administrative Information
Lectures: Tu., Thu., 14:00-15:15
Location: SN011
Instructor: Zhun Deng
Teaching Assistants: Xiaowei Yin Office hours: 3-4pm ET every Tuesday Location: Common area at SN Hall
Requirements: This is a graduate-level course that requires some mathematical background.
Required background: probability, discrete math, calculus, analysis, linear algebra, algorithms and data structures
Grading and Collaboration
Grading: Attendancy (15%), Assignments (40%), and a final course project (45%).
Collaboration: Collaboration on course projects are allowed.
Schedule
Lecture 1: Nash Equilibrium