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Instructor:
Dan Weld
Office hours: Fri 10:30 in CSE 588 or by email 
TA: Galen Andrew Office hours: Wed 1:003:00 in CSE 218 
TA: Naozumi Hiranuma Office hours: Tue 1:302:30 in CSE 218, Thu 1:002:00 in CSE 220 

TA: Travis Mandel Office hours: Fri 3:304:30 in CSE 218 

TA: Jeff Shaffer Office hours: Wed 10:3011:30 in CSE 021 
Find our class page at: https://piazza.com/washington/fall2014/cse473/
First time logging in? You should have gotten an activation email from the Piazza system. Alternatively, visit the link above and follow the instructions to enroll as a student in CSE 473.
Date  Topics & Lecture Notes  Readings 

September 24  Introduction, Agents  Optional: R&N, Ch. 1 & Ch. 2 
September 26  Problem Spaces & Blind Search  R&N, Ch. 3 thru p91 
September 29  BestFirst, UniformCost, Greedy & A* Search  R&N, Ch. 3, p 9298 
October 1  Heuristics & Pattern DBs  R&N, Section 3.6, (optional: pattern DB paper) 
October 3  Local Search  R&N, Section 4.1 
October 6  Constraint Satisfaction Problems I (.pdf, .pptx with demos)  R&N, Chapter 6 
October 8  CSPs, part II  None 
October 10  Adversary search, minmax  R&N Chapter 5 thru p174 
October 13  Alphabeta search  R&N, 5.3 & 5.4 
October 15  Expectimax search  R&N, 5.5, 5.7 & 5.9 
October 17  Markov decision processes (MDPs)  R&N, section 17.1 
October 20  Bellman equations (see previous slides)  
October 22  MDPs: value iteration  R&N, section 17.2 
October 24  MDPs: policy iteration (ppt)  R&N, section 17.2 
October 27  Midterm (Solutions)  Practice: Probs 1, 2, 3, 8b, 8c Probs 1a, 2, 3 R&N problems 6.4, 6.5 
October 29  Reinforcement Learning  R&N, section 21.1, 21.2 
October 31  RL Continued (QLearning)  R&N, section 21.1, 21.2 
November 3  RL Continued (Approximate QLearning)/Uncertainty  R&N sections 21.3 & 21.4 
November 5  Uncertainty: Inference by enumeration & Bayes Rule  R&N Ch 13 
November 7  Markov Models  R&N Sections 15.1 & 15.2 
November 10  Hidden Markov Models  R&N Sections 15.3 
November 12  No class  
November 14  Hidden Markov Models & the Forward Algorithm  
November 17  Particle Filters for HMMs  R&N 15.5 
November 19  Bayes Nets  R&N 14.1 & 14.2 
November 21  Independence in Bayes Nets  R&N 14.3 
November 24  Inference in Bayes Nets  R&N 14.4 
November 26  Variable Elimination, NP Completeness, Polytrees  
December 1  Parameter Learning  R&N Sections 18.1, 18.2, 20.1, 20.2 
December 3  Expectation maximization & learning Bayesnet structure  R&N Sections 20.2 & 20.3 
December 5  Summary 


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