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?
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?