Class Lectures: Tuesdays and Thursdays 10:30-11:50am in 4623 Wean Hall

This course is targeted at graduate students who want to learn about and perform current-day research in artificial intelligence---the discipline of designing intelligent decision-making machines. Techniques from probability, statistics, game theory, algorithms, operations research and optimal control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots surveying Antarctica, schedulers moving billions of dollars of inventory, spacecraft deciding which experiments to perform, or vehicles negotiating for lanes on the freeway. This AI course is a review of a selected set of these tools. The course will cover the ideas underlying these tools, their implementation, and how to use them or extend them in your research.


Students entering the class should have a pre-existing working knowledge of linear algebra, calculus, algorithms and data structures, and basic knowledge of computational complexity though the class has been designed to allow students with a strong numerate background to catch up and fully participate. Students should also be able to program in C, C++, or Java.

Mailing Lists



Homework Policy

Important Note: Since this is a graduate class, we expect students to want to learn and not google for answers. The purpose of problem sets in this class is to help you think about the material, not just give us the right answers. If you happen to use any material other than that in the text book or from the lectures, it must be acknowledged clearly with a citation on the submitted solution.

Collaboration Policy

Homeworks will be done individually: each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. In preparing your own writeup, you should not refer to any written materials from a joint study session. You also must indicate on each homework with whom you collaborated.

Late Homework Policy

Homework Regrading Policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to Michelle or Marilyn, and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

Final Project

Note to people outside CMU

Feel free to use the slides and materials available online here. If you use our slides, an appropriate attribution is requested. Please email the instructors with any corrections or improvements.