Lecture 
Date 
Topic 
Reading
(textbook) 
Notes 
1 
Sep 11 
Introduction (Lecture slides) 
[RN2] Chapt. 1 and 2 [RN3] Chapt. 1 and 2 

2 
Sep 13 
Uninformed Search (Lecture slides) 
[RN2] Sect. 3.13.5 [RN3] Sect. 3.13.4 

3 
Sep 18 
Informed Search (Lecture slides) 
[RN2] Sect. 4.14.2 (except RBFS) [RN3] Sect. 3.53.6 (except RBFS) 
A1 out 
4 
Sep 20 
No lecture 

5 
Sep 25 
Constraint Satisfaction Problems
(Lecture slides) 
[RN2] Sect. 5.15.2 [RN3] Sect. 6.16.3 

6 
Sep 27 
Local Search (Lecture slides) 
[RN2] Sect. 4.3 [RN3] Sect. 4.1 

7 
Oct 2 
Uncertainty (Lecture slides) 
[RN2] Sect. 13.113.6 [RN3] Sect. 13.113.5 

8 
Oct 4 
Bayesian Networks (Lecture slides) 
[RN2] Sect. 14.114.2 [RN3] Sect. 14.114.2 
A1 due, A2 out 
9 
Oct 9 
Bayesian Networks (Lecture slides) 
[RN2] Sect. 14.4 (except
clustering
algorithms) [RN3] Sect. 14.4 (except clustering algorithms) 

10 
Oct 11 
Decision Theory (Lecture slides) 
[RN2] Sect. 16.116.3 [RN3] Sect. 16.116.3 

11 
Oct 16 
Decision Networks (Lecture slides) 
[RN2] Sect. 16.516.6 [RN3] Sect. 16.516.6 
Project proposals due 
12 
Oct 18 
Probabilistic reasoning over
time (Lecture slides) 
[RN2] Chapt. 15.115.3, 15.5
(except approximate inference) [RN3] Chapt. 15.115.3, 15.5 (except approximate inference) 

13 
Oct 23 
Markov Decision Processes (Lecture slides) 
[RN2] Sect. 17.117.2, 17.417.5 [RN3] Sect. 17.117.2, 17.4 
A2 due 
14 
Oct 25 
Decision tree
learning (Lecture slides) 
[RN2] Sect. 18.118.3 [RN3] Sect. 18.118.3 
A3 out 
15 
Oct 30 
Statistical
Learning
(Lecture Slides) 
[RN2] Sect. 20.120.2 (up to p.
718) [RN3] Sect. 20.120.2 (up to p. 809) 

16 
Nov 1 
Midterm
(in
class:
45:20
pm) RCH 204: AJ (last name) RCH 211: KZ (last name) 
Midterm 

17 
Nov 6 
Statistical Learning (Lecture slides) 
[RN2] Sect. 20.3 (up to p. 731) [RN3] Sect. 20.3 (up to p. 823) 

18 
Nov 8 
Markov Networks (Lecture slides) 
Michael Jordan, Graphical Models, Statistical Science (Special Issue on Bayesian Statistics), 19, 140155, 2004.  
19 
Nov 13 
Conditional
random
fields
(Lecture slides) 
Hanna M. Wallach, Conditional Random Fields: An Introduction, Technical Report MSCIS0421, Department of Information Science, University of Pensylvania, 2004.  A3 due, A4 out 
20 
Nov 15 
First
order
logic
(Lecture slides) 
[RN2] Sect 7.17.6, Chapt. 8 and
9 [RN3] Sect 7.17.6, Chapt. 8 and 9 

21 
Nov 20 
Markov
Logic
Network
(Lecture slides) 
Matt Richardson and Pedro
Domingos (2006), Markov
Logic
Networks,
Machine
Learning, 62, 107136. 

22 
Nov 22 
Alchemy
package & Applications (Lecture
slides) 
Marc Summer and Pedro Domingos (2007), The Alchemy Tutorial, Department of Computer Science and Engineering, University of Washington.  
23 
Nov 27 
Learning and Inference with
Markov Logic Network (Lecture
slides) 

24 
Nov 29 
Course summary (Lecture slides)  A4 due 