===== Lectures =====


^Week ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1. |17. 2.  | ** Introduction** | VF | {{ :courses:becm33mlf:ls26_lecture_intro.pdf | }} | |
|2. |24. 2.  | ** Predictor evaluation **       | VF | {{ :courses:becm33mlf:ls26-lecture-predeval.pdf | }} | |
|3. |3. 3.   | ** Empirical Risk Minimization ** | VF |  | | 
|4. |10.3.   | ** Probably Approximately Correct Learning ** | VF | | |  
|5. |17. 3.  | ** Vapnik-Chervonenkis dimension. Structural Risk Minimization. **  | VF | | |
|6. |24. 3.  | ** Generative Learning  ** | VF |  |  |
|7. |31. 3.  | ** Linear Models ** | VF |  |  | 
|8. |7. 4.   | ** Support Vector Machines ** | VF |  |  |
|9. |14. 4 . | ** Kernel Methods ** | VF |  |  | 
|10.|21. 4.  | ** Unsupervised Learning ** | VF |  |  |
|11.|28. 4.  | ** Bayesian Learning ** | VF |  |  |
|12.|5. 5.   | **  ** | VF |  |  |
|13.|12. 5.  | **  ** | VF |  |  |
|14.|19. 5.  | ** Deep Learning and Generalization** | VF |  |  |