Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

This comprehensive machine learning textbook provides a rigorous introduction to the analysis and theory of algorithms, covering fundamental topics in modern machine learning.

With a focus on providing both theoretical foundations and practical applications, this book is an essential resource for graduate students and researchers in the field.

The authors present novel tools and concepts while giving concise proofs, making it an ideal textbook for those looking to deepen their understanding of machine learning algorithms.

New chapters on model selection, maximum entropy models, and conditional entropy models, as well as expanded coverage of concentration inequalities and information theory, make this second edition a valuable update for those already familiar with the first edition.

Whether you're looking to gain a deeper understanding of machine learning or simply want to stay up-to-date on the latest developments in the field, this textbook is an indispensable resource.