This book is a general introduction to machine learning that covers fundamental modern topics while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. Authors Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, and Francis Bachaim focuses on the analysis and theory of algorithms and present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.

Author: Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, and Francis Bach
Publisher: MIT Press
Date: December 2018
Pages: 504
ISBN: 978-0262039406
Print: 0262039400
Kindle: B07L3DFBVJ
Audience: Developers interested in Machine Learning
Level: Intermediate/Advanced
Category: Artificial Intelligence 

Topics covered include:

• the Probably Approximately Correct (PAC) learning framework
• generalization bounds based on Rademacher complexity and VC-dimension
• Support Vector Machines (SVMs)
• kernel methods
• boosting
• on-line learning
• multi-class classification
• ranking
• regression
• algorithmic stability
• dimensionality reduction
• learning automata and languages
• reinforcement learning

This second edition has three new chapters, on model selection, maximum entropy models, and conditional entropy models.

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