CSE573 Lecture Slides

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Lecture Slides [schedule subject to change]

Week Dates Topics & Slides Readings Notes

1 Sept 30 Introduction R&N Ch. 1, Ch. 2

2 Oct 5
Oct 7 Search
Heuristic search R&N Ch. 3.1-3.4
R&N Ch. 3.5-3.7

3 Oct 12
Oct 14 Game Trees: Minimax
Game Trees: Expectimax R&N Ch. 5.1-5.4
R&N Ch. 5.5-5.9 (5.6 is optional)

4 Oct 19
Oct 21 Markov Decision Processes (MDPs)
MDPs continued R&N Ch. 16.1-16.3; S&B Ch. 3
R&N Ch. 17.1-17.3; S&B Ch. 4

5 Oct 26
Oct 28 Reinforcement Learning (RL)
RL / Probability Review R&N 17.4 (17.4.2 optional)
Finish prev. readings

6 Nov 2
Nov 4 Hidden Markov Models (HMMs)
HMMs II R&N 15.1-15.3
R&N 15.5-15.6

7 Nov 9
Nov 11 Bayes Net (BNs)
No class (Veterans day) R&N 14.1-14.3
N/A

8 Nov 16
Nov 18 BN Inference
In class mid-term (Solutions) R&N 14.4-14.5
N/A

9 Nov 23
Nov 25 No class (Snow cancelation)
No Class (Thanksgiving)

10 Nov 30
Dec 2 Machine Learning (ML): Naive Bayes
ML: Perceptron R&N 18.1, 18.4
R&N 18.6

11 Dec 7
Dec 9 ML: Adv. Topics and Applications
Course Summary and Mini-project progress reports

Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX