9.520 | Spring 2003 | Graduate

Statistical Learning Theory and Applications

Calendar

There are three sessions, two Math Camps and an extra topic, at the bottom of the calendar. These will be given when students require the background needed to understand the next series of lectures and problems.

LEC # TOPICS
1 The Course at a Glance
2 The Learning Problem in Perspective
3 Regularization and Reproducing Kernel Hilbert Spaces
4 Regression and Least-Squares Classification
5 Support Vector Machines for Classification
6 Generalization Bounds, Intro to Stability
7 Stability of Tikhonov Regularization
8 Consistency and Uniform Convergence over Function Classes
9 Necessary and Sufficient Conditions for Uniform Convergence
10 Bagging and Boosting
11 Computer Vision, Object Detection
12 Loose Ends
13 Approximation Theory
14 RKHS, Mercer Thm, Unbounded Domains, Frames and Wavelets
15 Bioinformatics
16 Text
17 Regularization Networks
18 Morphable Models for Video
19 Leave-One-Out Approximations
20 Bayesian Interpretations
21 Multiclass Classification
22 Stability and Glivenko-Cantelli Classes
23 Symmetrization, Rademacher Averages
24 Project Presentations
25 Project Presentations
Math
Camp
Lagrange Multipliers/Convex Optimization
Math
Camp
Functional Analysis
Extra
Topic
SVM Rules of Thumb