There's an excellent course on PGM by Koller on Coursera. My friend took it and now he's a PGM evangelist. If you are wondering where PGM lies in the spectrum of machine learning, you should research the difference between generative and discriminate modeling. We have been driven to PGM to solve our ML problem that was hard to frame as A NN. Mainly because we had some priors we needed to encode to make the problem tr…
The hot use-case of the PGMs approach has often been in the discriminative setting (see M^3 and latent SVM) - which is good because discriminative classifiers work well with fewer data points (see Ng, Russel).