Probabilistic Machine Learning: An Introduction

An comprehensive introduction to probabilistic approaches in machine learning. This book covers fundamental concepts in probability theory, statistical inference, and their applications to modern machine learning algorithms. Murphy provides clear explanations of complex topics including Bayesian inference, graphical models, deep learning, and reinforcement learning, making it an essential resource for PhD students and researchers in the field.

The mathematical rigor combined with practical implementations in Python makes this an invaluable reference for understanding the theoretical foundations of machine learning while developing practical skills.