Along with updating all chapters and Python code examples, the second edition of this bestseller includes new chapters on Gaussian processes, Boltzmann machines, and deep belief networks. It also revises coverage of kernel methods and adds new material on random forests and model selection. The book retains its popular algorithmic approach as well as its focus on how to use the algorithms that make up machine learning methods and how and why these algorithms work.