Machine Learning for Physicists
Sadegh Raeisi, Sedighe Raeisi · 2023
This book presents machine learning (ML) concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialized computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some examples, and then proceeds to provide concrete examples with associated codes from a GitHub repository. Many of the code examples provided can be used as is or with suitable modification by the students for their own applications. Key features • Practical Hands-on approach: enables the reader to use machine learning. • Includes code and accompanying online resources. • Practical examples for modern research and uses case studies. • Written in a language accessible by physics students • Complete one-semester course.