Princeton University |
Computer Science 598D |
Spring
|
All readings available through blackboard (click on "course materials" then "readings").
# |
Date |
Reading |
Discussant |
| 0 | M 2/2 | ||
| 1 | Th 2/5 | Chapter 1: Introduction and overview | Jonathan Chang |
| 2 | Th 2/12 | Chapter 2: Mathematical study of machine learning | Berk Kapicioglu |
| 3 | Th 2/19 | Chapter 3: Using AdaBoost to minimize training error | Indraneel Mukherjee |
| 4 | Th 2/26 | Chapter 4: Direct bounds on the generalization error | Gungor Polatkan |
| 5 | Th 3/5 | Chapter 5: The margins explanation for boosting's effectiveness | Sean Gerrish |
| 6 | Th 3/12 |
Chapter 5: The margins explanation for boosting's effectiveness (cont.) Chapter 6: Game theory, on-line learning and boosting |
Sean Gerrish Taylor Xi |
| 7 | Th 3/19 | Chapter 6: Game theory, on-line learning and boosting (cont.) | Taylor Xi |
| 8 | Th 3/26 | Chapter 7: Loss minimization and generalizations of boosting | James Xiang |
| 9 | Th 4/2 | Chapter 8: Boosting, convex optimization and information geometry | Sina Jafarpour |
| 10 | Th 4/9 | Chapter 10: Optimal boosting and the continuous-time limit | Umar Syed |
| 11 | Th 4/16 | Chapter 11: Improved boosting using confidence ratings | Alex Schwing |
| 12 | Th 4/23 | Chapter 12: Multiclass classification problems | Berk Kapicioglu |
| 13 | Th 4/30 |
Chapter 13: Learning to rank (if time permitting, also finish Chapter 10) |
Gungor Polatkan |