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Today I am thinking about the graduate-level AI course I will be teaching next semester. This post is mainly for my own reference. I just want to store all the good course material I find in one place. The textbook I will use is the ever-famous AIMA (Artificial Intelligence: A Modern Approach) by Russell & Norvig. It is, as far as I know, the most widely-used AI textbook, and it is well-written.

I hesitated about posting the course websites here, lest students find them and somehow “cheat”. However, after thinking just for a few seconds, I decided not to worry. If they are diligent and interested enough to actually look at the course material in advance, all the better! I don’t have to teach them because they can learn from these other courses on the web! I just have to be careful not to assign homework or exam problems that have solutions on these webpages.

Many thanks to the instructors of these courses. I selected mainly US university courses published in the last couple of years.

Brown University, Spring 2008

UC Berkeley, Fall 2007

UC Berkeley, Fall 2008

CMU, Spring 2007

Georgia Tech, Fall 2007

Harvard, Fall 2008

UIUC, Spring 2007


I both fear and look forward to my first semester of “real” teaching.

I am teaching 1.33 courses next semester. The “1” is for a graduate AI course, and the “.33” is for an advanced undergraduate course that I am co-teaching with two colleagues. I have been thinking about the .33 course lately because we are drafting a course outline for it. The course will be called something like “CS and Probability” or “probabilistic (statistical?) methods in CS”, and as the name suggests, will cover probabilistic (statistical) methods used in solving problems in computer science. We decided to offer this new course because we felt the undergraduates needed a fun introduction to some of the current topics in AI research. (I know that the title does not sound very fun.)

My part of the course will be on statistical NLP. I plan to introduce some interesting problems in natural language processing, present a few empirical approaches, and have the students implement some of the well-known algorithms. There are two nice courses at CMU that I will use as references, though those courses, spanning 2 semesters for master’s level students, cover much much more than I will attempt. Thanks to Roni and Noah for the valuable course material.

Language & Statistics

Language & Statistics II