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.

Textbook and Readings

1. Algorithmic Game Theory by Nisan, Roughgarden, Tardos, Vazirani, available here

2. (Draft) Learning in Games (and Games in Learning) by Aaron Roth, available here

Schedule

• Lecture 1: Nash Equilibrium