(there was a link to an obsolete model exam here which I deleted; see the exam folder for an up to date one)

==== Lectures =====
**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: {{http://wasp-sweden.org/custom/uploads/2017/08/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: {{courses:smu: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.