Date 
Topic 
Notes 
Reading 
Jan 11 
Course overview. 


Jan 13 
Introduction to AI. 


Jan 18 
Problem solving as search. 


Jan 20 
Uninformed search; heuristic search. 


Jan 25 
A* search; local search. 


Jan 27 
Adverarial search. 


Feb 1 
Constraint satisfaction. 


Feb 3 
Constraint satisfaction (cont). 


Feb 8 
Knowledge representation. 


Feb 10 
Propositional logic; reasoning. 


Feb 15 
First order logic. 


Feb 17 
Logical inference. 


Feb 22 
Probability and uncertainty. 


Feb 24 
Representing uncertain knowledge. 


Mar 1 
Bayesian networks. 


Mar 3 
Bayesian networks (cont). 


Mar 8 
midterm review. 


Mar 10 
Exact inference algorithms. 


Mar 15 
SPRING BREAK; NO CLASS. 


Mar 17 
SPRING BREAK; NO CLASS. 


Mar 22 
Approximate inference algorithms. 


Mar 24 
Decision making. 


Mar 29 
Sequential decision making. 


Mar 31 
Markov decision process. 


Apr 5 
Markov decision process (cont). 


Apr 7 
Reinforcement learning. 


Apr 12 
Learning as search. 


Apr 14 
Learning (cont) 


Apr 19 
Relational learning. 


Apr 21 
Relational learning II. 


Apr 26 
Flexible topic. 


Apr 28 
Final review. 

