Lecture 
Date 
Topic 
Reading
(textbook) 
Notes 
1 
Jan 3 
Introduction (Lecture slides) 
[RN2] Chapt. 1 and 2 [RN3] Chapt. 1 and 2 

2 
Jan 5 
Uninformed Search (Lecture slides) 
[RN2] Sect. 3.13.5 [RN3] Sect. 3.13.4 
A1 out 
3 
Jan 10 
Informed Search (Lecture slides) 
[RN2] Sect. 4.14.2 (except RBFS) [RN3] Sect. 3.53.6 (except RBFS) 

4 
Jan 12 
Constraint Satisfaction Problems
(Lectures slides) 
[RN2] Sect. 5.15.2 [RN3] Sect. 6.16.3 

5 
Jan 17 
Local Search (Lecture slides) 
[RN2] Sect. 4.3 [RN3] Sect. 4.1 

6 
Jan 19 
Uncertainty (Lecture slides) 
[RN2] Sect. 13.113.6 [RN3] Sect. 13.113.5 

7 
Jan 24 
Bayesian Networks (Lecture slides) 
[RN2] Sect. 14.114.2 [RN3] Sect. 14.114.2 
A1 due, A2 out 
8 
Jan 26 
Bayesian Networks (Lecture slides) 
[RN2] Sect. 14.4 (except
clustering
algorithms) [RN3] Sect. 14.4 (except clustering algorithms) 

9 
Jan 31 
Decision Theory (Lecture slides) 
[RN2] Sect. 16.116.3 [RN3] Sect. 16.116.3 

10 
Feb 2 
Decision Networks (Lecture slides) 
[RN2] Sect. 16.516.6 [RN3] Sect. 16.516.6 

11 
Feb 7 
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) 

12 
Feb 9 
Project Ideas 


13 
Feb 14 
Markov Decision Processes (Lecture slides) 
[RN2] Sect. 17.117.2, 17.417.5 [RN3] Sect. 17.117.2, 17.4 
A2 due, A3 out 
14 
Feb 16 
Decision tree
learning (Lecture slides) 
[RN2] Sect. 18.118.3 [RN3] Sect. 18.118.3 
Project proposals due 
15 
Feb 28 
Statistical
Learning
(Lecture slides) 
[RN2] Sect. 20.120.2 (up to p.
718) [RN3] Sect. 20.120.2 (up to p. 809) 

16 
Mar 1 
No
lecture
(midterm
in
class)

Midterm 

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

18 
Mar 8 
Markov Networks (lecture slides) 
Michael Jordan, Graphical Models, Statistical Science (Special Issue on Bayesian Statistics), 19, 140155, 2004.  
19 
Mar 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 
Mar 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 
Mar 20 
Markov
Logic
Network
(Lecture slides) 
Matt Richardson and Pedro
Domingos (2006), Markov
Logic
Networks,
Machine
Learning, 62, 107136. 

22 
Mar 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 
Mar 27 
Learning and Inference with
Markov Logic Network (Lecture
slides) 

24 
Mar 29 
Course summary (Lecture slides)  A4 due 