Artificial Intelligence (CPS 270), Fall 2008

TuTh 11:40-12:55, LSRC D243
Instructor: Vincent Conitzer (please call me Vince). Office hour: Thursday 1pm-2pm (after class) or by appointment. Office: LSRC D207.
Teaching Assistant: Lirong Xia. Office hour: Tuesday 3pm-4pm or by appointment. Office: LSRC D343.
Textbook: Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig.

comfortable programming in language such as C (or C++) or Java
some knowledge of algorithmic concepts such as running times of algorithms; having some rough idea of what NP-hard means
some familiarity with probability (we will go over this from the beginning but we will cover the basics only briefly)
not scared of mathematics, some background in discrete mathematics, able to do simple mathematical proofs

If you do not have a standard undergraduate computer science background, the course may still be appropriate for you, but talk to me first. Well-prepared undergraduates are certainly welcome.

You do not need to have taken an undergraduate AI course (though of course it will help if you have).

Assignments: 35%
Midterm exams: 30%
Final exam: 30%
Participation: 5%

The quals pass depends only on the exams, in the same proportions: that is, for the quals pass, it's 50% midterm exams, 50% final exam. If you want to take the qualifying exam, it will be on Friday, August 22, 2:00pm - 5:00pm, LSRC D344.

For the homework assignments, you may discuss them with another person, but you should do your own writeup, programming, etc. This also means that you should not take extremely detailed notes during your meeting with the other person; if you can't remember what you talked about, you probably didn't really understand it...

This is the first time I am teaching this course so we will be flexible with the schedule. Each topic will probably take a number of lectures to finish.

Sometimes, a book chapter will include more information than what we cover in class; in those cases, for the purpose of exams, you are only responsible for what we covered in class.

For your convenience there are links to the chapters that are available online (which would be useful if you have an old edition of the book; the chapter correspondence is here).

Date Topic Materials
8/26 Introduction. Chapter 1.
Introduction slides: ppt, pdf.
8/28 - 9/16 Search. Constraint satisfaction and optimization problems. Chapters 3, 4, 5.
Search slides: ppt, pdf.
More search slides: ppt, pdf.
For more about linear and integer programming, you can go to the website of my course last semester; especially the introduction and branch and bound lecture notes might be useful.
Homework 1. Helper files: knight distances, counting the number of pairs of attacking superqueens.
9/16, 9/18 Game playing. Chapter 6.
Slides: ppt, pdf.
9/23-9/30 Logic. Chapters 7, 8, 9.
Propositional logic slides: ppt, pdf.
First-order logic slides: ppt, pdf.
Homework 2.
10/2, 10/7 Planning. Planning slides: ppt, pdf.
Chapter 11.
10/16-11/11 Probabilistic reasoning. Chapters 13-17.
Homework 3.
Probability slides: ppt, pdf.
Bayes nets slides: ppt, pdf.
Markov processes and HMMs slides: ppt, pdf.
11/13, 11/18, 11/20 Decision theory. Markov decision processes, POMDPs. Game theory. Chapters 18, 19, 20.
Homework 4.
Decision theory slides: ppt, pdf.
MDP/POMDP slides: ppt, pdf.
Game theory slides: ppt, pdf.
11/25 Machine learning. Chapters 18, 19, 20, 21. (You do not need to know this in great detail since we spent so little time on this in class, the chapters are just in case you're interested.)
Machine learning slides: ppt, pdf.
11/25 Wrapping up. Wrapping up slides: ppt, pdf.