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E-mail Message: I thought you might be interested in this item at http://www.worldcat.org/oclc/1041560990 Title: Foundations of machine learning Author: Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar Publisher: Cambridge, Massachusetts : The MIT Press, [2018] ISBN/ISSN: 9780262039406 0262039400 OCLC:1041560990

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Foundations of machine learning

Author:	Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
Publisher:	Cambridge, Massachusetts : The MIT Press, [2018]
Series:	Adaptive computation and machine learning.
Edition/Format:	Print book : English : Second editionView all editions and formats
Summary:	"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 Read more...
Rating:	(not yet rated) 0 with reviews - Be the first.
Subjects	Machine learning. Computer algorithms. Künstliche Intelligenz. View all subjects
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Details

Document Type:	Book
All Authors / Contributors:	Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar Find more information about:
ISBN:	9780262039406 0262039400
OCLC Number:	1041560990
Description:	xv, 486 pages : illustrations (some color) ; 24 cm.
Contents:	The PAC learning framework -- Rademacher complexity and VC-dimension -- Model selection -- Support vector machines -- Kernel methods - Boosting -- On-line learning -- Multi-class classification -- Ranking -- Regression -- Maximum entropy models -- Conditional maximum entropy models -- Algorithmic stability -- Dimensionality reduction -- Learning automata and languages -- Reinforcement learning -- Conclusion -- Appendices: Linear algebra review ; Convex optimization ; Probability review ; Concentration inequalities ; Notions of information theory.
Series Title:	Adaptive computation and machine learning.
Responsibility:	Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.

Abstract:

"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--Provided by publisher.

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