| 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 |
