Learning Theory First Principles by Bach Francis (32 results)

hardcover. Condition: Good. Bruise/tear to cover.

Hardback. Condition: Good. 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.…

Hardback. Condition: Good. 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.…

hardcover. Condition: Very Good.

hardcover. Condition: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

hardcover. Condition: As New. Complete and unmarked. All pages present and readable, binding tight. No highlighting, underlining, or writing anywhere in the text. Cover and spine show minimal to no shelf wear. May have a remainder mark, a price sticker or its residue, or a previous owner's name on the inside cover. Access codes, CDs, DVDs, and other bundled supplements may not be included and should be assumed used or missing unless the listing explicitly states otherwise.…

Condition: New.

Condition: New.

Hardcover. Condition: new. 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.…

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New.

Condition: New.

Hardcover. Condition: Brand New. 448 pages. 9.00x6.00x9.25 inches. In Stock.

hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: New.

Condition: New.

Condition: New.

Hardcover. Condition: New. ISBN:9780262049443.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Hardback. Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

Condition: NEW.

Condition: New. 2024. Hardcover. . . . . .

Hardback. Condition: New.

Condition: New. 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&eacuterieure. His research focuses on machine learning and optimization.A comprehensive and c.

Condition: New. 2024. Hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condition: new. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…