Dave Blei writes:

This course is cross listed in Computer Science and Statistics at Columbia University.

It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course.

Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming.

The course is open to PhD students in CS, EE and Statistics. However, it is appropriate for quantitatively-minded PhD students across departments. Please contact me [Blei] if you are a PhD student who is interested, but cannot register.

Research in probabilistic graphical models has forged connections between signal processing, statistics, machine learning, coding theory, computational biology, natural language processing, computer vision, and many other fields. In this course we will study the basics and the state of the art, with an eye on applications. By the end of the course, students will know how to develop their own models, compute with those models on massive data, and interpret and use the results of their computations to solve real-world problems.

Looks good to me!

can anyone recommend a mooc that covers these topics?

Currently the only mooc that covers relevant topics is the one entitled Probabilistic Graphical Models on Coursera, offered by Prof. Daphne Koller, Stanford University.

BTW, I reckon the scribe notes in the Princeton COS513 link might be fine.

If you meant a book that covers the topics then “Probabilistic Graphical Models: Principles and Techniques” by Daphne Koller seems to be quite popular.

However, I don’t have any experience with Graphical models. So can someone with experience, please explain in what sense does Bayes relate to Graphical model; is it rooted in Empirical Bayes, etc?

they are one and the same. graphs are representations of bayesian models.

@Anonymous

Not a mooc but regardless of mathematical level and previous experience I think that the book by E.J. Wagenmakers and Michael Lee (http://tinyurl.com/muw3wro) introduces graphical models together with discussion of real-life-like experimental data (remember we go to war on the data we have not the hypothetical data one could simulate to prove a point) and associated code in WinBUGS.

Tom Lodewyckx wrote a manual on how to make graphical models in LaTeX which can be downloaded from here: http://sites.google.com/site/tomlodewyckx/downloads/TutorialGMLTX.zip.

However the book of Wagenmakers and Lee contains a wast set of examples with models using discrete hyperparameters so translating all the models to STAN language is not trivial but I’ve found that most of the models can be translated with some extra tricks (although this comes sometimes with computational expense).