There are a few outstanding and very influential books on machine learning.
Note that there are many more covering other topics such as machine learning from a classical statistics perspective, or practical issue such as deep learning. These outstanding and highly influential books focus on core machine learning and Bayesian statistics concepts.
All are available as free PDFs (click on the book covers).
Bishop: Pattern Recognition and Machine Learning
The go-to reference for fundamental machine learning concepts. Covers regression, kernel methods, graphical models, and variational inference with clear explanations and practical examples. It’s never earth-shattering but it quickly gives you the essence of those methods.
MacKay: Information Theory, Inference and Learning Algorithms
A unique masterpiece by one of the brightest minds in the field. More advanced than Bishop, it offers profound insights into statistics, entropy, and information theory. Don’t miss the exceptional proof of the noisy channel theorem in §10.3.
Murphy: Machine Learning: A Probabilistic Perspective
Murphy’s books and survey articles are comprehensive references covering nearly all machine learning algorithms, though less groundbreaking than the others.
