(there was a link to an obsolete model exam here which I deleted; see the exam folder for an up to date one)
The lectures are given in English to all students.
| L | Date | Lecturer | Contents |
|---|---|---|---|
| 1 | 18.2. | FŽ | A General Framework for Learning |
| 2 | 25.2. | FŽ | On-Line Learning, Mistake-Bound Learning Model |
| 3 | 4.3. | FŽ | Batch Learning, PAC Learning model |
| 4 | 11.3. | FŽ | Learning first-order CNF's |
| 5 | 18.3. | FŽ | Learning first-order clauses |
| 6 | 25.3. | FŽ | (lecture 5 cont'd) |
| 7 | 1.4. | FŽ | Reinforcement learning |
| 8 | 8.4. | FŽ | (lecture 7 cont'd) |
| 9 | 15.4. | FŽ | Learning Bayesian networks |
| - | 22.4. | - | Easter Monday |
| 10 | 29.4. | FŽ | (lecture 9 cont'd) |
| 11 | 6.5. | OK | Probabilistic Programming, a non-technical introduction, slides: pp.pdf |
| 12 | 13.5. | OK | Probabilistic Logic Programming, Prof Luc De Raedt's slides: srl-pp-tutorial-wasp-stockholm.pdf, slides 1-42, 75-81 |
| 13 | 20.5. | OK | (lecture 12 cont'd) Prof Luc De Raedt's slides: 74-81, 96-119, additional WMC slides: wmc_intro.pdf |
Lecture slides: smu-slides.pdf (primary study material for lectures 1-10). Minor corrections in lecture slides posted on June 14. Re-download if you have an earlier version. Thanks to all students who reported bugs!
Both resources are under construction. Check back regularly for updates.