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