Lectures

Week Date Topic Lecturer Pdf Notes
1. 17. 2. Introduction VF
2. 24. 2. Predictor evaluation VF Begginer's guide
3. 3. 3. Empirical Risk Minimization VF Begginer's guide
4. 10.3. Probably Approximately Correct Learning VF Begginer's guide
5. 17. 3. Vapnik-Chervonenkis dimension. Structural Risk Minimization. VF Begginer's guide
6. 24. 3. Generative Learning VF Begginer's guide
7. 31. 3. Linear Models VF
8. 7. 4. Kernel Methods VF
9. 14. 4 . Support Vector Machines VF
10.21. 4. Model Selection VF
11.28. 4. Unsupervised Learning VF
12.5. 5. Bayesian Learning VF
13.12. 5. Deep Learning and Generalization VF
14.19. 5. Reserve VF
courses/becm33mlf/lectures.txt · Last modified: 2026/05/12 13:10 by xfrancv