Advanced Topics in Machine Learning and Game Theory (Fall 2020)

Basic Information

Course Name: Advanced Topics in Machine Learning and Game Theory
Meeting Days, Times, Location: MW at 9:00 am – 10:20 am in GHC 4303
Semester: Fall, Year: 2020
Units: 12, Section(s): 17599 (Undergrad), 17759 (Graduate), 17951 (PhD)


Instructor Information

Name Dr. Fei Fang
Contact Info Email:
Office location Wean Hall 4126
Office hours TBD





Course Description

This course is designed to be a graduate-level course covering the topics at the intersection of machine learning and game theory. Recent years have witnessed significant advances in machine learning and their successes in detection, prediction, and decision-making problems. However, in many application domains, ranging from auction and ads bidding, to entertainment games such as Go and Poker, to autonomous driving and traffic routing, to the intelligent warehouse, to home assistants and the Internet of Things, there is more than one agent interacting with each other. Game theory provides a framework for analyzing the strategic interaction between multiple agents and can complement machine learning when dealing with challenges in these domains. Therefore, in the course, we will introduce how to integrate machine learning and game theory to tackle challenges in multi-agent systems. The course consists of several parts and covers multiple topics as listed below

  • Part I: Basics of Machine Learning and Game Theory
    • Linear programming and duality
    • Introduction to game theory
    • Introduction to learning theory and reinforcement learning
  • Part II: Learning in Games
    • Learning rules in games and their convergence to equilibrium
    • Learning game parameters
  • Part III: Strategic Behavior in Learning
    • Adversarial Machine Learning (AML)
    • Learning from strategic data sources
  • Part IV: Multiagent Reinforcement Learning (MARL)
    • Classical algorithms in MARL
    • Recent advances in MARL
  • Part V: Applications of Machine Learning and Game Theory
    • Learning to combat adversaries for security and sustainability
    • Learning optimal auctions from samples

The course will be a combination of lectures, class discussions, and student presentations. Students will be evaluated on their class participation, paper reading assignment, paper presentations, and course projects. We will focus on mathematical foundations with rigorous proofs in class and the students may need to write code in their course projects. The course content is designed to not have too much overlap with other AI courses offered at CMU.


Prerequisites include linear algebra, probability, algorithms, and at least one course in artificial intelligence. Familiarity with optimization is a plus but not necessary. Please see the instructor if you are unsure whether your background is suitable for the course.

Learning Objectives

At the end of the course, the students should be able to

  • Describe and prove fundamental theoretical result in learning in games, strategic classification, and multi-agent reinforcement learning
  • Describe classical and recent algorithms at the intersection of machine learning and game theory
  • Describe the applications of techniques integrating machine learning and game theory
  • Deliver a report of course project and present the work through oral or poster presentation


Course Schedule (Subject to Change)

    Last update: 2/27/20

# Date Topic Slides and References


Learning Resources

No formal textbook. References and additional resources will be provided in slides and on Canvas.



The final course grade will be calculated using the following categories:

Assessment Percentage of Final Grade
Class Participation 10 points
Paper Reading Assignment 20 points
Paper Presentation 20 points
Course Project 50 points
  • Class Participation. The grading of the class participation will be mostly based on attendance, checked by in-class polls and asking and answering questions in class. Other factors include asking and answering questions on Canvas/Piazza.
  • Paper Reading Assignment. The course will require all students to complete weekly paper reading assignments individually and provide reading summaries.
  • Paper Presentation. Each student will be asked to present one paper.
  • Course Project. The students will work in small groups (2-3 students in each group) on a course project related to machine learning and game theory. The students are required to submit a project report through Canvas and deliver an oral or poster presentation. The progress of projects will be checked through the Project Proposal, Project Progress Report, Poster Presentation, and Final Project Report. The proposal and progress report will be peer-reviewed. The presentation and the final report will be evaluated by the instructor and TA directly. The final report will get a full score if it is at the same level as accepted papers at top AI conferences. For all the reports, students may choose a format with the guidance of their advisors or use the LNCS format or AAAI format.

Students will be assigned final letter grades according to the following table.

Grade Range of Points
A [90,100]
B [80,90)
C [70,80)
D [60,70)
R (F) [0,59)

Grading Policies

  • Late-work policy and Make-up work policy: No late days or make-up work is allowed. However, for paper reading assignments, the lowest scored assignment will be dropped.
  • Re-grade policy: To request a re-grade, the student needs to write an email to the instructor titled “Re-grade request from [Student’s Full Name]” within one week of receiving the graded assignment.
  • Attendance and participation policy: Attendance and participation will be a graded component of the course. The grading of the class participation will be mostly based on attendance, checked by in-class polls and asking and answering questions in class. Other factors include asking and answering questions on Canvas.


Course Policies

  • Academic Integrity & Collaboration: For paper reading assignments, the student can discuss with other students, but he needs to specify the names of the students he discussed with in the submission, and complete the summary on his own. For the course project, the students can discuss and collaborate with others (including students, faculty members), but the students need to give proper credits to whoever involved, and report the contributions of each group member in the final report and presentations, which will be considered in the grading. It is allowed to use publicly available code packages but the source of code package needs to be specified in the submission. Plagiarism is not allowed. The policy is motivated by CMU policy on academic integrity which can be found here.
  • Mobile Devices: Mobile devices are allowed in class. Cellphones should be in silent mode. Students who use tablet in an upright position and laptops will be asked to sit in the back rows of the classroom.
  • Accommodations for students with disabilities: If you have a disability and require accommodations, please contact Catherine Getchell, Director of Disability Resources, 412-268-6121, If you have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate.
  • Statement on student wellness: As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus here. Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.