Probabilistic Machine Learning: An Introduction
This is probably my favorite introductory machine learning book. The fact that he places almost everything in the language of graphical models is such a good common ground to build off.This really sets you up to realize that there is (and should be) a lot more to doing a good job in machine learning than simply minimizing an objective function. The answers you get depend on the model you create as do the questions you can hope to answer.I don't see a clear list of differences between this new edition. Does anyone know what's new?