In this book, we broaden the use of machine learning to more difficult issues.

We discuss methods for finding "insights" about data based on latent variable models, we discuss how to use probabilistic models for causal inference and decision-making under uncertainty, and we discuss the generation of high dimensional outputs, such as images, text, and graphs, so the output space is, say, Y = R256256.

We presuppose that the reader is familiar with ML and other pertinent mathematical concepts.

Nearly all of the figures can be reproduced using online Python code, which is primarily written in JAX.