Neural Networks and Statistical Learning

This book, now a fully updated and expanded third edition provides a comprehensive and accessible introduction to neural networks and statistical learning, two of the most important foundations of modern artificial intelligence. It presents a unified view of machine learning methods, combining classical statistical learning techniques with modern deep learning architectures and emerging AI technologies. Now in its third, fully revised and updated edition.

This book begins with the mathematical and theoretical foundations of machine learning, including optimization, learning theory, and statistical principles. It then introduces core neural network models and learning algorithms, covering topics such as multilayer perceptrons, recurrent neural networks, clustering methods, kernel learning, support vector machines, and probabilistic models. Building on these fundamentals, this book explores modern developments in artificial intelligence, including deep learning, graph neural networks, transformers, large language models, federated learning, and generative AI.

A distinctive feature of the book is its integrated treatment of theory, algorithms, and modern applications. Numerous illustrations, tables, and structured explanations are used to clarify complex concepts and provide readers with a clear understanding of how different learning methods are related. This new edition contains new advanced in artificial intelligence, including deep neural networks, graph neural networks, transformers, large language model AI (LLMs), federated learning, and generative AI (GenAI).

By presenting both classical approaches and recent advances within a single coherent framework, this book offers readers a systematic understanding of machine learning techniques and their role in modern intelligent systems. It serves as a valuable resource for students, researchers, and professionals working in artificial intelligence, data science, and machine learning.