Is Koller's "Probabilistic Graphical Models" suitable as a textbook? Or is there another book which is more recommendable as textbook for a master-course?
Disclaimer: cross-posted from quora.com, where I got no answer.
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Yes, it's written as such and contains sample questions, for which you can request the answers here
You might also want to have a look at Pattern Recognition and Machine Learning by Chris Bishop and Information Theory, Inference and Learning Algorithms by David MacKay, which can also be downloaded for free. Both of these cover some aspects of graphical models as well as giving a general insight into probabilistic methods.
I spent a little while reading the first couple of chapters of Koller & Friedman, and I wasn't happy with it as an introductory text. On several occasions, the book gives a motivating example, but the example cannot be understood without background material later in the chapter. This kind of exposition works for me only if the example explicitly says what upcoming material will be relevant; otherwise, the examples are just incomprehensible magic.
That said, it's a hefty tome, and probably an excellent reference for practitioners.
A student might have better luck with Neapolitan, "Learning Bayesian Networks".