| 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. 3.1-3.5 [RN3] Sect. 3.1-3.4 |
A1 out |
| 3 |
Jan 10 |
Informed Search (Lecture slides) |
[RN2] Sect. 4.1-4.2 (except RBFS) [RN3] Sect. 3.5-3.6 (except RBFS) |
|
| 4 |
Jan 12 |
Constraint Satisfaction Problems
(Lectures slides) |
[RN2] Sect. 5.1-5.2 [RN3] Sect. 6.1-6.3 |
|
| 5 |
Jan 17 |
Local Search (Lecture slides) |
[RN2] Sect. 4.3 [RN3] Sect. 4.1 |
|
| 6 |
Jan 19 |
Uncertainty (Lecture slides) |
[RN2] Sect. 13.1-13.6 [RN3] Sect. 13.1-13.5 |
|
| 7 |
Jan 24 |
Bayesian Networks (Lecture slides) |
[RN2] Sect. 14.1-14.2 [RN3] Sect. 14.1-14.2 |
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. 16.1-16.3 [RN3] Sect. 16.1-16.3 |
|
| 10 |
Feb 2 |
Decision Networks (Lecture slides) |
[RN2] Sect. 16.5-16.6 [RN3] Sect. 16.5-16.6 |
|
| 11 |
Feb 7 |
Probabilistic reasoning over
time (Lecture slides) |
[RN2] Chapt. 15.1-15.3, 15.5
(except approximate inference) [RN3] Chapt. 15.1-15.3, 15.5 (except approximate inference) |
|
| 12 |
Feb 9 |
Project Ideas |
|
|
| 13 |
Feb 14 |
Markov Decision Processes (Lecture slides) |
[RN2] Sect. 17.1-17.2, 17.4-17.5 [RN3] Sect. 17.1-17.2, 17.4 |
A2 due, A3 out |
| 14 |
Feb 16 |
Decision tree
learning (Lecture slides) |
[RN2] Sect. 18.1-18.3 [RN3] Sect. 18.1-18.3 |
Project proposals due |
| 15 |
Feb 28 |
Statistical
Learning
(Lecture slides) |
[RN2] Sect. 20.1-20.2 (up to p.
718) [RN3] Sect. 20.1-20.2 (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, 140-155, 2004. | |
| 19 |
Mar 13 |
Conditional
random
fields
(Lecture slides) |
Hanna M. Wallach, Conditional Random Fields: An Introduction, Technical Report MS-CIS-04-21, Department of Information Science, University of Pensylvania, 2004. | A3 due, A4 out |
| 20 |
Mar 15 |
First
order
logic
(Lecture slides) |
[RN2] Sect 7.1-7.6, Chapt. 8 and
9 [RN3] Sect 7.1-7.6, Chapt. 8 and 9 |
|
| 21 |
Mar 20 |
Markov
Logic
Network
(Lecture slides) |
Matt Richardson and Pedro
Domingos (2006), Markov
Logic
Networks,
Machine
Learning, 62, 107-136. |
|
| 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 |