Author Description

The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, including deep learning, viewed through the lens of probabilistic modeling and Bayesian decision theory. This second edition has been substantially expanded and revised, incorporating many recent developments in the field. It has new chapters on linear algebra, optimization, implicit generative models, reinforcement learning, and causality; and other chapters on such topics as variational inference and graphical models have been significantly updated. The software for the book (hosted on github) is now implemented in Python rather than MATLAB, and uses state-of-the-art libraries including as scikit-learn, Tensorflow 2, and JAX.