[pmbouman]

A few other things (sorry, not to snipe too much! :) )

-I'm skeptical of the idea of a single "standard text" in such a fast-moving field. New machine learning techniques appear constantly and are often documented online years before they appear in books. Some computer scientists say they prefer conference proceedings over academic journals because the latter take so long.

-Further, I'm not sure that the goal of any text should be to cover topics X, Y and Z in any case, which doesn't seem possible for a book to do. What does seem feasible is to set up a framework for analyzing the performance of different techniques. So I'd like to hear a comparison of how Bishop does that vs. HTF.

-You're of course correct that HTF takes a statistician's POV on the field - the authors are all professors of statistics at Stanford. They are also accomplished - Friedman was a co-author on CART, for example. I would instead ask the question: what can you get out of the book and the framework it offers?

-I think that part of the framework in machine learning is to think about bias AND variance, and how to trade them off successfully. This is an important part of model selection, for example.