| 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 |
