Lecture Notes

Each lecture summary below provides a brief description of the topics covered, as well as a list of suggested readings for more in-depth exploration. The slide presentations from many of the lectures are also included.

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