Machine Learning for Physicists
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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.
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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.
Copyright © IOP Publishing Ltd 2023
Online ISBN: 978-0-7503-4957-4 • Print ISBN: 978-0-7503-4955-0
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Recommend to your LibrarianMachine Learning for Physicists is a highly recommended resource for physics students eager to harness the power of machine learning in their research. Its practical orientation, relevant examples, and project-based learning approach make it an excellent starting point.
Dr. J. Rogel-Salazar, Contemporary Physics, Oct 2024