Machine Learning

A Physicist Perspective
Erscheinungsjahr 2022
ISBN: 978-1-77469-233-2
Verlag: Arcler Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 264 Seiten, Electronic book text, Format (B × H): 152 mm x 229 mm

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.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines

Inhaltsverzeichnis

- Chapter 1 Fundamentals of Machine Learning
- Chapter 2 Physical Aspects of Machine Learning in Data Science
- Chapter 3 Statistical Physics and Machine Learning
- Chapter 4 Particle Physics and Cosmology
- Chapter 5 Machine Learning in Artificial Intelligence
- Chapter 6 Materials Discovery and Design Using Machine Learning
- Chapter 7 Machine Learning and Quantum Physics
- Chapter 8 Modern Applications of Machine Learning

Über Autor(innen)

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 physicsfrom 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.