===== Lectures =====


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