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"Kevin Murphy's book on machine learning is a superbly written, comprehensive treatment of the field, built on a foundation of probability theory. It is rigorous yet readily accessible, and is a must-have for anyone interested in gaining a deep understanding of machine learning." "This is a remarkable book covering the conceptual, theoretical and computational foundations of probabilistic machine learning, starting with the basics and moving seamlessly to the leading edge of this field. The pedagogical structure of the book is extremely useful for teaching. One of my favorite parts is that most of the figures of the book have a link to the associated (python, JAX, tensorflow) code that is used to generate them, often with comparisons between the different computational ways of solving the problems." "This book could be titled'What every ML PhD student should know'.