Hardcover. A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.Provides a balanced and unified treatment of most prevalent machine learning methodsEmphasizes practical application and features only commonly used algorithmic frameworksCovers modern topics not found in existing texts, such as overparameterized models and structured predictionIntegrates coverage of statistical theory, optimization theory, and approximation theoryFocuses on adaptivity, allowing distinctions between various learning techniquesHands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors "The aim of this book is to provide the simplest formulations that can be derived "from first principles" with simple arguments"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Learning Theory from First Principles (Hardcover)
Editor: MIT Press Ltd
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 10: 0262049449
ISBN 13: 9780262049443
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.

Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.

Provides a balanced and unified treatment of most prevalent machine learning methods

Emphasizes practical application and features only commonly used algorithmic frameworks
Covers modern topics not found in existing texts, such as overparameterized models and structured prediction
Integrates coverage of statistical theory, optimization theory, and approximation theory
Focuses on adaptivity, allowing distinctions between various learning techniques
Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors

Acerca del autor

Francis Bach is a researcher at Inria where he leads the machine learning team which is part of the Computer Science department at Ecole Normale Sup�rieure. His research focuses on machine learning and optimization.

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