| Date |
Topic |
Reading |
Handout |
| 9/17 |
Intro |
Ch. 1 |
[ppt |
ps |
pdf ]
|
| 9/19 |
Search |
Ch. 3 [3.3, 3.5] |
[ppt |
ps |
pdf ]
|
| 9/24 |
Heuristic Search |
Ch. 4 [4.1, 4.2] |
[ppt |
ps |
pdf ]
|
| 9/26 |
Constraint Sat. |
[3.7] |
[ppt |
ps |
pdf ]
|
| 10/1 |
Satisfiability |
Ch. 6 [6.4, ex. 6.15] |
[ppt |
ps |
pdf ] |
| 10/3 |
Sat. Encodings |
|
[ppt |
ps |
pdf ] |
| 10/8 |
Local Search |
Ch. 4 [4.4], B. 3.1, B, [20.8] |
[ppt |
ps |
pdf ] |
| 10/10 |
Game trees |
Ch. 5 [5.2, 5.3, 5.4] |
[ppt |
ps |
pdf ] |
| 10/15 |
Catch up day |
|
|
| 10/17 |
Games of chance |
[5.5] |
[ppt |
ps |
pdf ] |
| 10/22 |
Markov Models |
|
[ppt |
ps |
pdf ] |
| 10/24 |
Midterm |
|
|
| 11/5 |
Language and learning |
Ch. 22 |
[ppt |
ps |
pdf ] |
| 11/7 |
Probability and IR |
Ch. 14 [14.2], Ch. 23 [23.1], Optional Reading |
[ppt |
ps |
pdf ] |
| 11/12 |
Sequence models |
[24.7] |
[ppt |
ps |
pdf ] |
| 11/14 |
Hidden Markov Models |
[23.2] |
[ppt |
ps |
pdf ] |
| 11/19 |
Expectation Maximization |
|
[ppt |
ps |
pdf ] |
| 11/21 |
Catch up day |
|
|
| 11/26 |
Supervised Learning |
Ch. 18 [18.3] |
[ppt |
ps |
pdf ] |
| 11/28 |
Neural Networks |
Ch. 19 [19.3, 19.4] |
[ppt |
ps |
pdf ] |
| 12/3 |
Backpropagation |
|
[ppt |
ps |
pdf ] |
| 12/5 |
Latent Semantic Indexing |
Ch. 15 [15.1, 15.2] |
[ppt |
ps |
pdf ] |
| 12/10 |
More Probabilistic Models |
Ch. 19 [19.6] |
[ppt |
ps |
pdf ] |
| 12/12 |
Wrap up |
|
[ppt |
ps |
pdf ] |
