Boli´var Nelson. Machine Learning: A Physicist Perspective

Arcler Press, 2021-12-01. — 264 p. — ISBN: 978-1-77469-233-2.Deep learning and machine learning have gained significant importance in the last few years. New inventions and discoveries are taking place every day to exploit the concepts of machine-learning techniques. This book aims to present the fundamentals of machine learning with an emphasis on deep learning, neural networks, and physical aspects of machine learning. The design of materials and molecules with desired features is an essential prerequisite for progressing technology in our contemporary societies. This necessitates the capability to compute precise microscopic characteristics, such as forces, energies, and efficient selection of potential energy faces, to attain corresponding macroscopic features. Tools required to achieve the above-mentioned goals can be extracted from quantum mechanics, statistical mechanics, and classical physics, respectively. To overcome the challenge of technology integration, significant efforts are being made to speed up quantum physical simulations with the help of machine learning. This evolving interdisciplinary community consists of material scientists, chemists, physicists, computer scientists, and mathematicians, coming together to contribute to the exciting field of machine learning and artificial intelligence. This book can be used as a reference material for acquiring the fundamentals of machine learning from a physicist’s perspective. Moreover, people from all backgrounds can benefit from this introductory book on Machine Learning.