Lectures

Week Date Topic Lecturer Pdf Notes
1.17. 2. Introduction VF
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
courses/becm33mlf/lectures.txt · Last modified: 2026/02/23 16:33 by xfrancv