| Lecture |
Date |
Topic |
Reading
(textbook) |
Notes |
| 1 |
Sept 9 |
Introduction (slides: 1/page 6/page) |
Chapt. 1 and 2 |
|
| 2 |
Sept 11 |
Uninformed Search (slides: 1/page 6/page) |
Sect. 3.1-3.5 |
|
| 3 |
Sept 16 |
Informed Search (slides: 1/page 6/page) |
Sect. 4.1-4.2 (except RBFS) |
A1 out |
| 4 |
Sept 18 |
Constraint Satisfaction Problems
(slides: 1/page 6/page) |
Sect. 5.1-5.2 |
|
| 5 |
Sept 23 |
Propositional Logic (slides: 1/page 6/page) |
Sect 7.1-7.6 |
Slide 18 corrected on Nov 13 |
| 6 |
Sept 25 |
First-order logic (slides: 1/page 6/page) |
Chapt. 8 and 9 |
|
| 7 |
Sept 30 |
Probability Theory (slides: 1/page 6/page) |
Sect. 13.1-13.6 | |
| 8 |
Oct 2 |
Bayesian Networks (slides: 1/page 6/page) |
Sect. 14.1-14.2 | A1 due, A2 out |
| 9 |
Oct 7 |
Bayesian Networks (slides: 1/page 6/page) |
Sect. 14.4 (except clustering algorithms) | |
| 10 |
Oct 9 |
Project ideas |
||
| 11 |
Oct 14 |
Decision Theory (slides: 1/page 6/page) |
Sect. 16.1-16.3 | |
| 12 |
Oct 16 |
Decision Networks (slides: 1/page 6/page) |
Sect. 16.5-16.6 | Project proposals due |
| 13 |
Oct 21 |
Decision trees
learning
(slides: 1/page 6/page) |
Sect. 18.1-18.3 | |
| 14 |
Oct 23 |
Statistical Learning
(slides: 1/page 6/page)
|
Sect. 20.1-20.2 (up to p. 718) | A2 due, A3 out |
| 15 |
Oct 28 |
Statistical Learning (slides: 1/page 6/page) | Sect. 20.3 (up to p. 731) | |
| 16 |
Oct 30 |
Ensemble learning (slides: 1/page 6/page) | Sect. 18.4 | |
| 17 |
Nov 4 |
No lecture (midterm in class) |
Midterm |
|
| 18 |
Nov 6 |
Probabilistic reasoning over time (slides: 1/page 6/page) | Chapt. 15 (p. 537-542,549,559) | |
| 19 |
Nov 11 |
Markov Networks (slides: 1/page 6/page) | Michael Jordan, Graphical
Models, Statistical Science (Special Issue on Bayesian Statistics),
19, 140-155, 2004. |
|
| 20 |
Nov 13 |
Conditional Random Fields (slides: 1/page 6/page) | 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 Slide 7 corrected on Nov 25 |
| 21 |
Nov 18 |
Markov Logic Network (slides: 1/page 6/page) | Matt Richardson and Pedro
Domingos (2006), Markov
Logic Networks, Machine Learning, 62, 107-136. |
|
| 22 |
Nov 20 |
Alchemy package & Applications (slides: 1/page 6/page) | Marc Summer and Pedro Domingos (2007), The Alchemy Tutorial, Department of Computer Science and Engineering, University of Washington. | |
| 23 |
Nov 25 |
Learning and Inference with Markov Logic Networks (slides: 1/page 6/page) | ||
| 24 |
Nov 27 |
Course summary (slides: 1/page 6/page) | A4 due |