Supplementing the previously published book Probabilistic Machine Learning. Introduction," this classic work introduces the reader to the details of the most relevant theories and methods of machine learning, including deep generative models, graph models, Bayesian inference, reinforcement learning, and causality. Deep learning is presented in the context of a broader statistical context, and deep learning approaches are unified with probabilistic modeling and inference approaches.