IE598: Syllabus
Sewoong Oh, University of Illinois Urbana-Champaign
Here is a rough syllabus (changes are possible, and suggestions/feedback are welcome).
| week 1 | 1.Overview, 2.Graphical models |
| week 2 | 3.Markov Property |
| week 3 | 4.Elimination algorithm/Belief propagation |
| week 4 | 4.BP 5.Density evolution |
| week 5 | 6. Max-product algorithm 7.Gaussian graphical models |
| week 6 | 7. Gaussian graphical models, (No class Sep 29th - Allerton conference) |
| week 7 | Mid-term quiz, Application: Crowdsourcing |
| week 8 | Application: Diffusion, 8.Variational inference |
| week 9 | 8.Variational inference |
| week 10 | (No class Oct 27th - traveling) |
| week 11 | 9.Markov chain Monte Carlo, 10 Learning |
| week 12 | 11.Restricted Boltzmann machines |
| week 13 | |
| week 14 | (No class - Thanks Giving) |
| week 15 | |
| week 16 |
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