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

Book Details:

• Author: Mehryar Mohri

• Publisher: MIT Press

• Edition: Second Edition

• Binding: Hardcover

• Number of Pages: 504

• ISBN: 9780262039406

• Release Date: 25-12-2018

• Languages: English

• Color: White

• Package Dimensions: 9.2 x 7.2 x 1.3 inches

About the Book:

"Foundations of Machine Learning" by Mehryar Mohri is an advanced, graduate-level textbook that offers a rigorous introduction to machine learning through the lens of algorithm analysis and theory. This second edition, updated with three new chapters and numerous new exercises, is ideal for both graduate students and researchers looking to dive deep into the theoretical foundations of machine learning algorithms.

The book systematically explores the mathematical and theoretical aspects of modern machine learning, starting with the foundational concepts and progressing to more advanced topics. It delves into the Probably Approximately Correct (PAC) learning framework, Support Vector Machines (SVMs), kernel methods, boosting, online learning, multi-class classification, regression, and reinforcement learning, among other topics.

The second edition includes valuable new content such as chapters on model selection, maximum entropy models, and conditional entropy models. The appendices have also been expanded to include new sections on Fenchel duality, concentration inequalities, and a comprehensive introduction to information theory.

Rich with exercises, this textbook is not only an excellent resource for students but also an invaluable reference for practitioners and researchers in the field of machine learning, offering conceptual clarity and insightful analysis for those eager to understand and apply cutting-edge algorithms.