Networks for Learning: Regression and Classification

Calendar

LEC #	TOPICS
1	The Course at a Glance
2	The Learning Problem in Perspective
3	Regularized Solutions
4	Reproducing Kernel Hilbert Spaces
5	Classic Approximation Schemes
6	Nonparametric Techniques and Regularization Theory
7	Ridge Approximation Techniques
8	Regularization Networks and Beyond
9	Applications to Finance
10	Introduction to Statistical Learning Theory
11	Consistency of the Empirical Risk Minimization Principle
12	VC-Dimension and VC-bounds
13	VC Theory for Regression and Structural Risk Minimization
14	Support Vector Machines for Classification
15	Project Discussion
16	Support Vector Machines for Regression
17	Current Topics of Research I: Kernel Engineering
18	Applications to Computer Vision and Computer Graphics
19	Neuroscience I
20	Neuroscience II
21	Current Topics of Research II: Approximation Error and Approximation Theory
22	Current Topics of Research III: Theory and Implementation of Support Vector Machines
23	Current Topics of Research IV: Feature Selection with Support Vector Machines and Bioinformatics Applications
24	Current Topics of Research V: Bagging and Boosting
25	Selected Topic: Wavelets and Frames
26	Project Presentation