In the seminar, we will discuss solutions to theoretical assignments. These assignments will be made available in advance, and students are encouraged to work on them beforehand.
During the seminars, we will also assign and explain the practical homework tasks. Students will be required to implement solutions and submit their code for automatic evaluation. Additionally, some assignments will involve writing a report that includes responses to the given problems. These reports will be evaluated manually.
| Week | Date | Topic | Teacher | Materials | Notes / Homeworks | Deadline |
|---|---|---|---|---|---|---|
| 1 | 25. 9. | seminar cancelled | ||||
| 2 | 2. 10. | Seminar: Predictor Evaluation | VF/JP | (with solutions) | Assignment of HW 1 | 6.11.2025 |
| 3 | 9. 10. | Seminar: Probably Approximately Correct Learning | VF/JP | (with solutions) | HW 2 | 13.11.2025 |
| 4 | 16. 10. | Seminar: VC dimension | VF/JP | (with solutions) | ||
| 5 | 23. 10. | Seminar: Neural Networks | JD/JP | |||
| 6 | 30. 10. | seminar cancelled | ||||
| 7 | 6. 11. | Lab: Neural Networks | JD | |||
| 8 | 13. 11. | Seminar: Support Vector Machines | VF/JP | |||
| 9 | 20. 11. | Seminar: Ensembling | JD/JP | |||
| 10 | 27. 11. | Seminar: Gradient Boosting Machine | JD/JP | |||
| 11 | 4. 12. | Seminar: Generative learning, Maximum Likelihood estimator | VF/JP | |||
| 12 | 11. 12. | Seminar: EM algoritm; Bayesian learning | VF | |||
| 13 | 18. 12. | Seminar: (Hidden) Markov Models | JD/JP | |||
| 14 | 8. 1. | reserved |