Course Information

Course Info EECS 598-006, Fall 2013, 3 Credits
Instructor Jacob Abernethy, 3765 BBB, jabernet_at_umich_dot_edu
Time, Place MW 1:30-3pm, FXB 1008 (Updated!)
The Details Course Schedule, Topics and Links
Office Hours Tuesdays 1-2pm

Course Description

This course will focus on the problem of prediction, learning, and decision making, yet the underlying theme will involve game playing, betting and minimax analysis. We will begin by introducing the classical Weighted Majority Algorithm, and more broadly the problem of “adversarial online learning” and “regret minimization”, and this will launch us into topics such as von Neumann’s Minimax Theorem, multi-armed bandit problems, Blackwell Approachability, calibrated forecasting, and proper scoring rules. I intend to spend some time on applications to finance, like repeated gambling, universal portfolio selection, and option pricing.

Prerequisites: Familiarity with the analysis of algorithms, probabilistic analysis, and several similar topics. EECS 545 (Machine Learning) will be quite helpful but not strictly necessary. The material is going to be about 90% "theory" and thus potential students must have a strong mathematical background. We shall rely heavily on techniques from calculus, probability, and convex analysis, but many tools will be presented in lecture.

Coursework: There will be a small number of problem sets, and the final project for the course will consist of the option to do independent research or to give a literature review presentation to the class.

Grade Breakdown

35% for Homeworks There will be 3-4 problem sets through the semester
50% for Final Project [(NEW!!) Summary of Project Ideas] Students can do a final project on reviewing some research paper, doing novel research, or implementing some algorithms in an interesting way. More details on this to come.
15% for Participation Students must scribe a lecture, participate in class, and can receive participation credit for answering some challenge questions. I will try to make enough opportunities for this.


The course will not have any official textbook. But the following book (which influenced the choice of title for the course) will be quite helpful:

  • "Prediction, Learning, and Games," by Nicolo Cesa-Bianchi and Gabor Lugosi

There is another text that has a few chapters I would like to cover:

  • "Probability and Finance: It's Only a Game!" by Glen Shafer and Vladimir Vovk

In the last several years, several surveys have come out that explore several topics that we shall cover. I will link to them here, and will mention them in various lectures when appropriate:

Scribe Notes

  1. Lecture 1, 9/4: Course Overview and Intro to Online Learning
  2. Lecture 2, 9/9: Weighted Majority Algorithm
  3. Lecture 3, 9/11: The Exponential Weights Algorithm
  4. Lecture 4, 9/16: The Action Setting and Hyperexperts
  5. Lecture 5, 9/18: The Fixed-Share Forecaster
  6. Lecture 6, 9/23: Lower Bounds and Game Theory I
  7. Lecture 7, 9/25: Game Theory II: Nash Equilibria and von Neumann
  8. Lecture 8, 9/30: Game Theory III: Proof of Minimax Thm using Hedge Alg
  9. Lecture 9, 10/02: Applications of Minimax: LP and Boosting
  10. Lecture 10, 10/07: Boosting and Perceptron Algorithms
  11. Lecture 11, 10/09: Perceptron and Universal Portfolio Selection
  12. Lecture 12, 10/16: Online Convex Optimization
  13. Lecture 13, 10/21: Universal Portfolios Review and Online Convex Optimization
  14. Lecture 14, 10/23: Game-theoretic Probability in Finance
  15. Lecture 15, 10/28: Online Convex Optimization: Part III
  16. Lecture 16, 10/30: Follow the Regularized Leader
  17. Lecture 17, 11/04: FTRL and Applications of OCO
  18. Lecture 18, 11/06: The Bandit Setting
  19. Lecture 19, 11/11: UCB Algorithm and the Adversarial Bandit Problem
  20. Lecture 20, 11/13: EXP3 Algorithm
  21. Lecture 21, 11/18: Bandit Algorithm and Blackwell Approachability
  22. Lecture 22, 11/20: Blackwell's Approachability Theorem
  23. Lecture 23, 12/02: B.A.T. Review and Calibrated Forecasting
  24. Lecture 24, 12/04: Generalized Calibration and Correlated Equilibria


  1. Homework #1 - Due 9/25/2013
  2. Homework #2 - Due 10/28/2013 which requires constraints.csv defining a feasibility problem.
  3. Homework #3 - Due 11/27/2013

Course Schedule + Additional Info

Students can see this document HERE for a full-page experience. You can also view the embedded iframe doc below BUT if you want links to open in a new page you must CTRL-click (or CMD-click on a mac). Pardon the inconvenience, I am not sure if this can be fixed.