Princeton University
Computer Science Department

Computer Science 402
Artificial Intelligence

Rob Schapire

Fall 2008
Directory
General Information | Schedule & Readings | Assignments | moodle | blackboard

Schedule and readings

Numbers in brackets under "readings" refer to chapters or sections of Russell & Norvig.

#

Date

Topic

Readings (required)

Other (optional) readings and links
1 Th 9/11 General introduction to AI. [1]

AI Growing Up by James Allen (but skip or skim page 19 to end).

 AAAI website with LOTS of readings on AI in general, AI in the news, etc.

Robocup website.  The simulation league movies can be found here.  (Click on "results", and then on the "F" next to any match.)  Four-legged robot league movies can be found here.

Videos of autonomous helicopter.
2 Tu 9/16 Uninformed (blind) search [3.1-3.5]  
3 Th 9/18 Informed (heuristic) search [4.1-4.2]  
4 Tu 9/23 Local search; searching in games [4.3], [6] (but okay to skip [6.5]) play checkers with Chinook
5 Th 9/25 Propositional logic [7.1-7.4]  
6 Fr 9/26
OR
Tu 9/30		 Theorem proving and the resolution algorithm [7.5]  
7 Th 10/2 Practical methods of solving CNF sentences [7.6]  
8 Tu 10/7 Applications of solving CNF sentences, including planning ;
Cursory look at first-order logic;
Uncertainty and basics of probability 		[11.1, 11.5] ;
[8.1-8.3] (okay to skim these);
[13.1-13.4] 		 
9 Th 10/9 Guest lecture: Gilbert Harman, Professor of Philosophy, on "AI and Philosophy" [26]
lecture notes		  
10 Tu 10/14 Independence and Bayes rule [13.5-13.6] "What is the chance of an earthquake?"  (article on interpreting probability, by Freedman & Stark)
11 Th 10/16 Bayesian networks: semantics and exact inference [14.1-14.4] brief tutorial on Bayes nets (and HMM's), with links for further reading
12 Tu 10/21 Approximate inference with Bayesian networks [14.5]
13
14		 Th 10/23
Tu 11/4		 Uncertainty over time (temporal models; HMM's)
Kalman filters		 [15.1-15.3]
formal derivations (optional)
[15.4]
15 Th 11/6 DBN's; particle filters; speech recognition [15.5-15.6] The particle filtering demo came from here on Sebastian Thrun's website. 

The sample speech signal came from here.

"I'm sorry Dave, I'm afraid I can't do that" (article on natural language processing by L. Lee)
16 Tu 11/11 Finish speech recognition; decision theory; begin Markov decision processes  [16.1-16.3]; [17.1]  
17
18		 Th 11/13
Tu 11/18		 Markov decision processes: Bellman equations, value iteration, policy iteration [17.2-17.4] Sutton & Barto's excellent book on reinforcement learning and MDP's
19 Th 11/20 Machine Learning
Decision trees		 [18.1-18.2]
[18.3]		  
20 Tu 11/25 Computational learning theory [18.5]
generalization error theorem proved in class		 original "Occam's Razor" paper
21 Tu 12/2 Boosting [18.4]
boosting slides
face slide
training error proof		 boosting overview paper
22 Th 12/4 Support-vector machines [20.6] tutorial on SVM's
23 Tu 12/9 Neural networks
Learning Bayes net and HMM parameters		 [20.5]
[20.1-20.3]		 A demo of LeNet, a neural network for optical-character recognition, is available here.  Click the links on the left to see how it does on various inputs.  The figure shows the activations of various layers of the network, where layer-1 is the deepest.  (For more detail, see the papers on the LeNet website, such as this one.)
24 Th 12/11 Reinforcement learning in MDP's [21.1-21.4] Sutton & Barto's excellent book on reinforcement learning and MDP's

Learning to play keepaway in robocup soccer using reinforcement learning.  Scroll down to find flash demos.