The book builds on the author’s 2012 work(MLAPP), expanding coverage of machine learning through the lens of probabilistic modeling and Bayesian decision theory. Driven by the deep learning revolution, the author split the second edition into two volumes to include major advances like deep learning, generative models, variational inference, and reinforcement learning. The new edition is more student-friendly, adding background material, exercises, and Python-based code (instead of Matlab). Together, these two volumes aim to comprehensively reflect the state of ML as of 2021, offering both foundational and advanced insights.