Foundations of Machine Learning

 
9780262039406: Foundations of Machine Learning
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Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
ISBN 10 : 0262039400 ISBN 13 : 9780262039406
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Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
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Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
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Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet
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Mehryar Mohri
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Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
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Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
ISBN 10 : 0262039400 ISBN 13 : 9780262039406
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Description du livre Etat : New. Brand New Original US Edition.We Ship to PO BOX Address also. EXPEDITED shipping option also available for faster delivery.This item may ship fro the US or other locations in India depending on your location and availability. N° de réf. du vendeur AUDECC-88044

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8.

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Edité par MIT Press Ltd, United States (2019)
ISBN 10 : 0262039400 ISBN 13 : 9780262039406
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Description du livre MIT Press Ltd, United States, 2019. Hardback. Etat : New. second edition. Language: English. Brand new Book. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.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.This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. N° de réf. du vendeur AAH9780262039406

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9.

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Edité par MIT Press Ltd, United States (2019)
ISBN 10 : 0262039400 ISBN 13 : 9780262039406
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The Book Depository
(London, Royaume-Uni)
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Description du livre MIT Press Ltd, United States, 2019. Hardback. Etat : New. second edition. Language: English. Brand new Book. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.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.This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. N° de réf. du vendeur AAH9780262039406

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10.

Mehryar Mohri (author), Afshin Rostamizadeh (author), Ameet Talwalkar (author)
Edité par The MIT Press 2019-04-02, Cambridge, Massachusetts (2019)
ISBN 10 : 0262039400 ISBN 13 : 9780262039406
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Blackwell's
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Description du livre The MIT Press 2019-04-02, Cambridge, Massachusetts, 2019. hardback. Etat : New. N° de réf. du vendeur 9780262039406

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