Machine Learning: A Physicist Perspective

Product Description

Deep-learning and machine-learning have gained a significant importance in the last few years. New inventions and discoveries are taking place every day to exploit the concepts of machine-learning technique. The aim of this book is to present the fundamentals of machine-learning with an emphasis on deep-learning, neural networks and physical aspects of machine learning. Design of materials and molecules with desired features is an essential prerequisite for progressing technology in our contemporary societies. This necessitates both 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 fundamentals of machine learning from a physicist’s perspective. Moreover, people from all backgrounds can benefit from this introductory book on Machine Learning.

About the Author

Nelson Bolivar is currently a Physics Professor in the Physics Department at the Universidad Central de Venezuela, where he has been teaching since 2007. His interests include quantum field theory applied in condensed matter. He obtained his PhD in physics from the Universite de Lorraine (France) in 2014 in a joint PhD with the Universidad Central de Venezuela. His BSc in physics is from the Universidad Central de Venezuela.