Schedule

The following schedule is possibly optimistic, but is definitely tentative and subject to revision, depending on how we progress. Ideally, we'll touch on all of the topics given here, but we may dwell longer on some and less on others. Some of the flow of the class will be subject to your interests.

Note that actual assignment due dates may differ from those given here. The due date given on the assignment takes precedence.

Section 0: Optimization and Search

Week 1-4 (Jan 21-Feb 13)

Intro; administrivia; a brief history of AI; AI as optimization; optimization as search; combinatorics; search algorithms; heuristics; A*, other modes of optimization.

Feb 4

Readings/discussion 1

Feb 13

Homework 1 due

Section 1: Representation, Planning, and Theorem Proving

Week 5-7 (Feb 18-Mar 6)

State space representations; predicate calculus; production systems; unification/resolution; STRIPS; reactive planning.

Feb 27

Reading/discussion 2

Mar 6

Homework 2 due

Section 2: Learning

Week 8-11 (Mar 11-Apr 10)

The machine learning framework; instance based learning; decision trees; neural networks; clustering; Markov decision processes; reinforcement learning.

Mar 13

Midterm exam

Mar 27

Graduate project proposals due

Apr 1

Happy new moon! Reading/discussion 3

Apr 3

Homework 3 due

Section 3: Game Playing

Week 12-13 (Apr 15-Apr 24)

Game tree search; minimax; alpha-beta; game theory; stochastic games; Nash equilibrium.

Apr 15

(Un)happy tax day!

Apr 17

Homework 4 due

Apr 24

Reading/discussion 4

Section 4: Special topics, final project presentations

Week 14-15 (Apr 29-May 8)

Catch up; review; project presentations; special topics (time permitting).

May 8

Final project writeups due (start of class!); last day of classes.

Final Exam Week

Final to be scheduled.