1 Introduction and scope  
2 Reasoning: goal trees and problem solving  
3 Reasoning: goal trees and rule-based expert systems  
4 Search: depth-first, hill climbing, beam Problem set 0 due
5 Search: optimal, branch and bound, A*  
6 Search: games, minimax, and alpha-beta Problem set 1 due
  Quiz 1  
7 Constraints: interpreting line drawings  
8 Constraints: search, domain reduction  
9 Constraints: visual object recognition Problem set 2 due
10 Introduction to learning, nearest neighbors  
11 Learning: identification trees, disorder  
  Quiz 2  
12 Learning: neural nets, back propagation Problem set 3 due
13 Learning: genetic algorithms  
14 Learning: sparse spaces, phonology  
15 Learning: near misses, felicity conditions  
16 Learning: support vector machines Problem set 4 due
  Quiz 3  
17 Learning: boosting  
18 Representations: classes, trajectories, transitions  
19 Architectures: GPS, SOAR, Subsumption, Society of Mind  
20 The AI business  
21 Probabilistic inference I  
  Quiz 4  
22 Probabilistic inference II Problem set 5 due
23 Model merging, cross-modal coupling, course summary  

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