CRF statistical models Yeah, so, read ITILA, and do all problems up to 3, and attempt as many 4 as possible. You can skip the info. theory half and go straigh

CRF statistical models Yeah, so, read ITILA, and do all problems up to 3, and attempt as many 4 as possible. You can skip the info. theory half and go straight to Bayesian stats and then neural nets - but you'll want to go back and learn the info theory too, eventually. BTW, you're jumping into an enormous kettle of fish called "graphical models," which are very useful but endlessly varied upon and often needlessly overcomplicated. I think what you really need is Zoubin: http://videolectures.net/mlss07_ghahramani_grafm/ http://videolectures.net/mlss07_ghahramani_grafm/ If that's a bit heavy, browse around the site for an easier introduction. I get ... how they work, but the embedded probability stuff is mostly squiggles to me Pardon me for saying so, but no, if you don't understand why each of those equations is what it is, then you don't understand how it works. That's kind of like saying "I get how the car works, but that bit in the front is just a giant hunk of metal to me." If it looks like squiggles, you need to untangle it. It's like untying a giant knot. You need to find some end, some piece of thread you can tug on, and then slowly tease each equation apart until you get how the various pieces move. (What happens if Z is bigger/smaller than what it is? Why is it there at all? What is a "clique"? Why would I add up potential functions in a clique?)