Lecture Notes

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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)