Princeton University 
Computer Science 402 

Chapters or sections of the Russell & Norvig text ("R&N") are listed for both the 3rd and 2nd editions. Take care to do the appropriate reading for the edition that you are using. Other additional required or optional readings and links are also listed below.
This syllabus is constantly evolving as the semester progresses, so check back often (and let me know if it seems not to be up to date).
# 
Date 
Topic 
Readings (required) 
Other (optional) readings and links 

R&N

R&N

other 

1  Th 9/15  General introduction to AI.  1  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, and the humanoid league. The video shown in class is here. 

2  Tu 9/20  Uninformed (blind) search  3.13.4  3.13.5  
3  Th 9/22  Informed (heuristic) search  3.53.6  4.14.2  
4  Tu 9/27 
Local search; Searching in games 
4.1 5 (ok to skip 5.55.6) 
4.3 6 (ok to skip 6.5) 
"The Chess Master and the Computer" by Garry Kasparov play checkers with Chinook 

5  Th 9/29  Guest lecture: Prof. Christopher Clark (Cal Poly) on "Underwater Robotics"  slides  
6  Tu 10/4  Propositional logic  7.17.4  7.17.4  
7  Th 10/6  Theorem proving and the resolution algorithm  7.5  7.5  handout on converting to CNF  
8  Tu 10/11  Practical methods of solving CNF sentences  7.6  7.6  
9  Th 10/13 
Applications of solving CNF sentences, including planning; Cursory look at firstorder logic; Uncertainty and basics of probability 
7.7; 10.1; 10.4.1; 8.18.3 (ok to skim); 13.113.3 
11.1; 11.5; 8.18.3 (ok to skim); 13.113.4 

10  Tu 10/18  Independence and Bayes rule  13.413.5  13.513.6  "What is the chance of an earthquake?" (article on interpreting probability, by Freedman & Stark)  
11  Th 10/20  Bayesian networks: semantics and exact inference  14.114.4  14.114.4  "Introduction to probabilistic topic models" by David Blei  brief tutorial on Bayes nets (and HMM's), with links for further reading  
12  Tu 10/25  Approximate inference with Bayesian networks  14.5  14.5  
13 14 
Th 10/27 Tu 11/8 
Uncertainty over time (temporal models; HMM's); Kalman filters  15.115.4  15.115.4  formal derivations (optional)  
15  Th 11/10  DBN's; particle filters; speech recognition 
15.5; 15.6 in 2nd ed. (available on ereserves via blackboard) 
15.515.6 
The particle filtering demo came from
here on
Sebastian Thrun's website. "I'm sorry Dave, I'm afraid I can't do that" (article on natural language processing by L. Lee) 

16  Tu 11/15 
Finish speech recognition; decision theory; begin Markov decision processes 
16.116.3 (ok to skim); 17.1 
16.116.3 (ok to skim); 17.1 

17 18 
Th 11/17 Tu 11/22 
Markov decision processes: Bellman equations, value iteration, policy iteration  17.217.4.1  17.217.4  sample runs of value iteration and policy iteration  Sutton & Barto's excellent book on reinforcement learning and MDP's  
19  Tu 11/29 
Machine Learning Decision trees 
18.118.4 
18.118.3; 18.4 in 3rd ed. (available on ereserves via blackboard) 

20  Th 12/1  Theory of learning  18.5  18.5  generalization error theorem proved in class  original "Occam's Razor" paper  
21  Tu 12/6  Boosting  18.10  18.4  slides 
training error proof boosting overview paper 

22  Th 12/8  Supportvector machines  18.9  20.6  tutorial on SVM's  
23  Tu 12/13 
Neural networks Learning Bayes net and HMM parameters 
18.7 20.120.3 
20.5 20.120.3 
A demo of LeNet, a neural network for opticalcharacter 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 layer1 is the deepest. (For more detail, see the papers on the LeNet website, such as this one.)  
24  Th 12/15  Reinforcement learning in MDP's  21.121.4  21.121.4 
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. 