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Instructor:
Dan Weld
(weld at cs dot washington dot edu) Office hours: Wed 10:3011:20 CSE 588 or by email 
TA:
Lydia Chilton
(hmslydia at cs dot washington dot edu) Office hours: Tuesday 3:304:30, CSE 006 computer cluster 
TA: Yisong Song
(titanium at cs dot washington dot edu) Office hours: Thursday 3:304:30, CSE 006 computer cluster 
Date  Topics & Lecture Notes  Readings 

March 26  Introduction, Agents  Optional: R&N, Ch. 1 & Ch. 2 
March 28  Problem Spaces & Blind Search  R&N, Sections 3.1 to 3.4 
March 30  Heuristic Search  R&N, Section 3.5 
April 2  Heuristics and Pattern Databases  R&N, Section 3.6 
April 4  Constraint Satisfaction  R&N, Section 6.16.3 
April 6  Local (Stochastic) Search  R&N, Section 4.1 
April 9  Constraint Satisfaction, Part II  R&N, Section 6.16.3 
April 11  Adversary Search (Minimax)  R&N, Section 5.15,2 
April 13  Adversary Search (AlphaBeta)  R&N, Section 5.35.4 
April 16  Adversary Search (Expetimax)  R&N, Section 5.5 
April 18  KR: Propositional Logic  R&N, Chapter 7 thru Section 7.5 
April 20  KR: DPLL, Walksat  R&N, Chapter 7 thru Section 7.6 
April 23  FirstOrder Logic  R&N, Sections 8.28.3.2, 8.4; 9.19.3.2 
April 25  Automated Planning  R&N, Chapter 10 through Section 10.2 
April 27  Automated Planning  R&N, Sections 10.310.5 
April 30  Markov Decision Processes  R&N, Chapter 17 through Section 17.1 
May 2  Markov Decision Processes  R&N, Section 17.2 
May 4  Reinforcement Learning  R&N, Chapter 21 thru 21.3 
May 7  Reinforcement Learning 2  R&N, Section 21.4 
May 9  POMDPs  R&N, Section 17.4 
May 11  Uncertainty, Markov Models  R&N, Chapter 13, Section 15.1 
May 14  Hidden Markov Models  R&N, Section 15.215.3 
May 16  Particle Filters  R&N, Section 15.5.3 
May 18  Bayesian Networks  Semantics  R&N, Chapter 14 thru Section 14.3 
May 23  Bayesian Networks  Inference  R&N, Sections 14.4 and 14.4 
May 25  Bayesian Networks  Parameter Learning  R&N, Chapter 18 thru 18.2; Chapter 20 thru 20.2.1; Section 20.2.4 
May 30  Bayesian Networks  Hyrbid Networks, Naive Bayes, and Structure Learning  R&N, Section 20.2 
June 1  Summary & Expectation Maximization  R&N, Section 20.3 


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