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Statistical Learning Theory and Applications >> Content Detail



Lecture Notes



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




 
 


 



 








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