(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. A General Framework for Learning
2 25.2. On-Line Learning, Mistake-Bound Learning Model
3 4.3. Batch Learning, PAC Learning model
4 11.3. Learning first-order CNF's
5 18.3. Learning first-order clauses
6 25.3. (lecture 5 cont'd)
7 1.4. Reinforcement learning
8 8.4. (lecture 7 cont'd)
9 15.4. Learning Bayesian networks
- 22.4. - Easter Monday
10 29.4. (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!

Textbook: smu-textbook.pdf (more detail but incomplete and slightly outdated).

Both resources are under construction. Check back regularly for updates.

courses/smu/lectures.txt · Last modified: 2019/06/15 00:08 by zelezny