Foundations of Machine Learning, Second Edition

Foundations of Machine Learning, Second Edition

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.

Publication date: 25 Dec 2018

ISBN-10: 0262039400

ISBN-13: 9780262039406

Paperback: 504 pages

Views: 1,030

Type: Textbook

Publisher: The MIT Press

License: The MIT License (MIT)

Post time: 05 Feb 2021 12:00:00

Foundations of Machine Learning, Second Edition

Foundations of Machine Learning, Second Edition A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
Tag(s): Machine Learning
Publication date: 25 Dec 2018
ISBN-10: 0262039400
ISBN-13: 9780262039406
Paperback: 504 pages
Views: 1,030
Document Type: Textbook
Publisher: The MIT Press
License: The MIT License (MIT)
Post time: 05 Feb 2021 12:00:00
Summary/Excerpts of (and not a substitute for) the The MIT License (MIT):
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

Click here to read the full license.
From the Book Description:

Mehryar Mohri wrote:This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning 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. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.

Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. 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; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.

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About The Author(s)


Mehryar Mohri is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University known for his work in machine learning, automata theory and algorithms, speech recognition and natural language processing.

Mehryar Mohri

Mehryar Mohri is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University known for his work in machine learning, automata theory and algorithms, speech recognition and natural language processing.


Afshin is a research scientist at Google Research NY, where he specializes in designing and applying machine learning algorithms. He received his BS in Electrical Engineering and Computer Science from UC Berkeley, his PhD in Computer Science from the Courant Institute at NYU with advisor Mehryar Mohri and was a post-doc at UC Berkeley in Peter Bartlett's group.

Afshin Rostamizadeh

Afshin is a research scientist at Google Research NY, where he specializes in designing and applying machine learning algorithms. He received his BS in Electrical Engineering and Computer Science from UC Berkeley, his PhD in Computer Science from the Courant Institute at NYU with advisor Mehryar Mohri and was a post-doc at UC Berkeley in Peter Bartlett's group.


Ameet Talwalkar is an assistant professor in the Machine Learning Department at CMU, and also co-founder and Chief Scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to automation, fairness, interpretability, and federated learning.
 

Ameet Talwalkar

Ameet Talwalkar is an assistant professor in the Machine Learning Department at CMU, and also co-founder and Chief Scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to automation, fairness, interpretability, and federated learning.
 


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