Learning Theory from First Principles
by
Francis Bach
Book Details
Format
Hardback or Cased Book
ISBN-10
0262049449
ISBN-13
9780262049443
Publisher
MIT Press Ltd
Imprint
MIT Press
Country of Manufacture
US
Country of Publication
GB
Publication Date
Dec 24th, 2024
Print length
478 Pages
Weight
1,104 grams
Dimensions
18.80 x 24.30 x 3.80 cms
Product Classification:
Information technology: general issues
Ksh 13,700.00
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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.
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
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