9.520 | Spring 2006 | Graduate

Statistical Learning Theory and Applications


TP = Prof. Tomaso Poggio
AR = Sasha Rakhlin
AC = Andrew Caponnetto
RR = Dr. Ryan Rifkin

1 The Course at a Glance TP
2 The Learning Problem in Perspective TP
3 Reproducing Kernel Hilbert Spaces AC
4 Regression and Least-Squares Classification RR
5 Support Vector Machines for Classification RR
6 Manifold Regularization AC
7 Unsupervised Learning Techniques AC
8 Multiclass RR
9 Ranking Guest Lecturer: Giorgos Zacharia
10 Boosting and Bagging AR
11 Computer Vision

Object Detection

Guest Lecturer: Stan Bileschi
12 Online Learning Guest Lecturer: Sanmay Das and AC
13 Loose Ends

Project Discussions

14 Generalization Bounds

Introduction to Stability

15 Stability of Tikhonov Regularization AR
16 Uniform Convergence Over Function Classes AR
17 Uniform Convergence for Classification


18 Neuroscience Guest Lecturer: Thomas Serre
19 Symmetrization

Rademacher Averages

20 Fenchel Duality Guest Lecturer: Ross Lippert and RR
21 Speech / Audio Guest Lecturer: Jake Bouvrie
22 Active Learning Guest Lecturer: Claire Monteleoni
23 Morphable Models for Video Guest Lecturer: Tony Ezzat
24 Bioinformatics Guest Lecturer: Sayan Mukherjee
25 Project Presentations  
26 Project Presentations (cont.)  
  Math Camp 1: Functional Analysis AC
  Math Camp 2: Probability Theory AR

Course Info

As Taught In
Spring 2006