Princeton University
Computer Science Department

Computer Science 598D
Boosting: Foundations and Algorithms

Rob Schapire

Spring 2009


Directory
General Information | Schedule & Readings | blackboard

Schedule and readings

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