Summary

This course will survey Artificial Intelligence. We will cover the most important algorithms and methods from this field and discuss application areas such as robotics, computer vision, and natural language processing.

Topics will include:

• Problem solving using search
• Game playing
• Logical reasoning and representation
• Probabilistic reasoning under uncertainty
• Reinforcement learning and Markov decision processes
• Machine learning

Lectures

Tuesday/Thursday 11:00AM-12:20PM, location CS 105

Course Staff

Professor

David M. Blei
204 CS Building
blei [at] cs.princeton.edu
659-258-9907
Office hours: Tue 12:30PM-2:00PM CS204

Teaching Assistant

Jonathan Chang
004 CS Building
jcone [at] princeton.edu
659-258-1785
Office hours: Mon 3:00PM-4:30PM CS004

Teaching Assistant

Berk Kapicioglu
223 CS Building
bkapicio [at] princeton.edu
267-679-5941
Office hours: Wed 3:00PM-4:30PM CS223

Textbook

Russell, S. and Norvig, P.
Artificial Intelligence: A Modern Approach
Second edition
Prentice Hall, 2003

Be sure to get the second edition. The changes from the first edition are substantial. Copies of this book are on reserve at the Engineering Library.

Mailing list

This list will be used by the course staff for general announcements such as last minute corrections to the homeworks and changes in due dates. This list can also be used by students for discussing course material and homeworks.

Jonathan and I will monitor and respond to questions on this list. If your question is specific to your own work, please contact me or Jonathan directly.

You can post to the list by sending mail to cos402@lists.cs.princeton.edu. Note that you can only post to the list using the email address you used to subscribe to it.

Prerequisites

COS 217 and 226, or permission of instructor.

Although there are no formal math prerequisites, students should have taken enough math as to feel comfortable with the mathematical treatment that will be given of much of the material. Calculus and linear algebra are sufficient, as is COS 341 or a course on probability and statistics. Come see me if you are unsure.

Course Grades and Workload

The course consists of lectures, readings, homework assignments, and a final exam.

Homework assignments will be given about once every two weeks and will be a mix of written exercises and programming. Homeworks count for 65% of your grade.

Anything covered in lecture or in the assigned readings is "fair game" for the final. You can expect that the emphasis will be placed on the same topics that were emphasized in lecture. The final exam counts for 35% of your grade.

Failure to complete any significant component of the course may result in a grade of D or F, regardless of performance on the other components. Final grades may be adjusted slightly upward for class participation.

You can track how well you are doing using moodle. As a rough guide, if the total number of points you get on your homeworks falls close to or below 65% of the total points possible, then you may be heading for a final grade of D or F and you should certainly seek assistance.

See the assignments page for important information on the late policy and collaboration policy, and for information about how the homeworks are graded.

Assignment Policies

Handing in. Please submit code to moodle, where you can also retrieve your grades. Written exercises must be handed in as hard copy to David Blei's office (CS 204).

Late days. Each student has 7 free late days which he or she can use for any assignment. (Weekends count as a single day.) After those days are used up, the assignment's grade is reduced by 20% for each day late. We will make exceptions for emergencies.

Grading. Homeworks are graded on getting the right answer or getting the program to work. In many homeworks, there are some more "free form" questions asking for exploration and experimentation. These questions will be graded more subjectively (as in the humanities). Ideal answers are thoughtful, perceptive, critical, clear, and concise.