In this book 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.

<ASIN:‎ 0262049449>

Author: Francis Bach
Publisher: The MIT Press
Date: December 2024
Pages: 496
ISBN: 978-0262049443
Print: ‎ 0262049449
Kindle: ‎ B0CYZNZ55J
Audience: General
Level: Intermediate/Advanced
Category: Methodology

• 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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