Description
Machine Learning: A Probabilistic Perspective offers a unified treatment of machine learning based on the principles of probability and statistical inference. Rather than presenting learning algorithms as isolated techniques, it demonstrates how diverse methods can be understood within a common probabilistic framework, providing deeper insight into their assumptions, strengths, and limitations. Combining rigorous mathematical exposition with practical examples, the book spans both foundational and advanced topics, making it an authoritative reference for graduate study and research in machine learning, artificial intelligence, and data science.
