9.520 | Spring 2006 | Graduate

Statistical Learning Theory and Applications

Lecture Notes

All materials are courtesy of the person named and are used with permission.

SES # TOPICS SUMMARY SLIDES
1 The Course at a Glance (PDF) (PDF - 8.10 MB)
2 The Learning Problem in Perspective (PDF) (PDF)
3 Reproducing Kernel Hilbert Spaces (PDF) (PDF)
4 Regression and Least-Squares Classification (PDF) (PDF)
5 Support Vector Machines for Classification (PDF) (PDF)
6 Manifold Regularization (PDF) (PDF)
7 Unsupervised Learning Techniques (PDF) (PDF)
8 Multiclass (PDF) (PDF)
9 Ranking (PDF) (PDF)
10 Boosting and Bagging (PDF) (PDF)
11

Computer Vision

Object Detection

   
12 Online Learning (PDF) (PDF)
13

Loose Ends

Project Discussions

   
14

Generalization Bounds

Introduction to Stability

(PDF) (PDF)
15 Stability of Tikhonov Regularization (PDF) (PDF)
16 Uniform Convergence Over Function Classes (PDF) (PDF)
17

Uniform Convergence for Classification

VC-dimension

(PDF) (PDF)
18 Neuroscience (PDF) (PDF - 2.5 MB)
19

Symmetrization

Rademacher Averages

   
20 Fenchel Duality    
21 Speech / Audio    
22 Active Learning   (PDF)
23 Morphable Models for Video    
24 Bioinformatics    
25 Project Presentations    
26 Project Presentations (cont.)    
  Math Camp 1: Functional Analysis   (PDF)
  Math Camp 2: Probability Theory   (PDF)

Course Info

Instructor
As Taught In
Spring 2006
Level