Math Optimization for Artificial Intelligence: Heuristic and Metaheuristic Methods for Robotics and Machine Learning (Mathematical Methods in the Digital Age, 2)

Math Optimization for Artificial Intelligence:Heuristic and Metaheuristic Methods for Robotics and Machine Learning (Mathematical Methods in the Digital Age, 2)

Math Optimization for Artificial Intelligence:Heuristic and Metaheuristic Methods for Robotics and Machine Learning (Mathematical Methods in the Digital Age, 2)

by: Umesh Kumar Lilhore (Editor), Vishal Dutt (Editor), T. Ananth Kumar (Editor), Martin Margala (Editor), Kaamran Raahemifar (Editor)

Publisher: De Gruyter

Edition: 1st

Publication Date: 2025-04-21

Language: English

Print Length: 430 pages

ISBN-10: 3111436055

ISBN-13: 9783111436050

Book Description

The book presents powerful optimization approaches for integrating AI into daily life.This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology.This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead.

Editorial Reviews

The book presents powerful optimization approaches for integrating AI into daily life.This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology.This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead.

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