Description
The rapid advancement of technology and data-driven innovation has propelled Machine Learning (ML) to the forefront of modern science and engineering. No longer confined to computer science alone, ML has become an essential tool across disciplines—including mathematics, physics, electronics, electrical engineering, and beyond. This book, Integrating Machine Learning into Science and Engineering, is born out of a shared vision to bridge the gap between theoretical concepts and practical applications of ML across multiple scientific and technical domains. It is designed not only for researchers and practitioners but also for students and educators who are looking to understand how ML techniques can be effectively applied to solve real-world problems in STEM fields. We have aimed to present a comprehensive yet accessible exploration of ML methods, with clear examples, interdisciplinary case studies, and practical insights. Whether it is using ML in mathematical modeling, optimizing physical systems, enhancing signal processing in electronics, or improving control systems in electrical engineering, each chapter is crafted to illuminate the powerful synergy between machine learning and core scientific principles. The content of this book is the result of collaborative efforts among the authors, each bringing their expertise from diverse yet interconnected domains.
