I have been taking a break from deep learning, and I am quite into graphical models (GM) lately. So that's why I am gathering resources of understanding various concepts of GM.
Here are some useful courses one can use. They are not sorted/categorized, it's just useful for me to look them through later.
Note that except Koller's class, not all of the following classes have video available.
- Daphne Koller's Probabilistic Graphical Models on Coursera. This is perhaps the best yet the most difficult one. All quiz and exams are filled with trick questions which can challenge even very experienced MLers.
- The Modern Stanford's version taught by Stefano Ermon Also check out the class notes, which is quite accessible.
- The Brown's class, I found the tutorial lectures are quite useful. It also points to various book chapters for different concepts.
- A Short Course on Graphical Models by Mark A. Paskin, this is interesting because it's more a short-3 lecture class to cover most basic concepts.
- PRML Chapter9
- Koller's "Probabilistic Graphical Models: Principles and Techniques" - fairly dense, but yet again it seems to have the best information.
- Michael Jordan's unfinished book on Graphical Model.