Learning Theory from First Principles (Adaptive Computation and Machine Learning series)

 

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.

The book offers a comprehensive introduction to the foundations and modern applications of learning theory. It is designed to provide readers with a solid understanding of the most important principles in machine learning theory, covering a wide range of topics essential for both students and researchers. 

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.

Content Highlights

Francis Bach presents the foundations and latest advances of learning theory, making complex mathematical arguments accessible to newcomers. The book is structured to guide readers from basic concepts to advanced techniques, ensuring a thorough grasp of the subject matter.

Key Features of the Book

Comprehensive Coverage of Learning Theory:

The book provides a detailed exploration of fundamental principles and modern advances in learning theory.

It includes a blend of classical topics (e.g., empirical risk minimization, generalization bounds) and cutting-edge approaches in machine learning.

Mathematical Rigor with Accessibility:

While mathematically rigorous, the book is designed to be accessible to newcomers, ensuring a strong foundation for readers with minimal prior exposure to advanced mathematics.

Complex arguments are presented in a way that is clear and easy to follow, catering to a diverse audience.

Focus on First Principles:

The book emphasizes understanding concepts from first principles, allowing readers to develop an intuitive and theoretical grasp of learning algorithms and their behavior.

This approach helps build a strong, conceptual framework for tackling real-world machine learning challenges.

Wide Range of Topics:

The book covers various topics in learning theory, including:

Generalization bounds and sample complexity.

Optimization in machine learning.

Probabilistic models and statistical learning.

Regularization techniques and their role in controlling complexity.

It integrates theoretical insights with practical applications.

Step-by-Step Progression:

The content is structured to guide readers step-by-step, starting with the basics and progressing toward advanced topics.

This makes it suitable for both beginners and advanced readers.

Target Audience

This textbook is ideal for graduate students and researchers who aim to acquire a basic mathematical understanding of the most widely used machine learning architectures. It serves as a valuable resource for those looking to deepen their knowledge in machine learning theory and its practical applications.

Who Should Read This Book?

Graduate students and researchers in machine learning and AI.

Professionals aiming to deepen their understanding of learning theory.

Academics teaching machine learning theory courses.

Self-learners interested in a mathematically solid understanding of ML concepts.

Hard Copy: Learning Theory from First Principles (Adaptive Computation and Machine Learning series)

Kindle: Learning Theory from First Principles (Adaptive Computation and Machine Learning series)