Overview

This book presents 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 specialised 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

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9780750349550

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Reviews

Machine 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

Author Information

Sadegh Raeisi has a background in Quantum Computing and Quantum Information Science. He completed his MSc at the University of Calgary and his Ph.D. at the Institute for Quantum Computing at the University of Waterloo, as well as a Postdoc at the Max Planck Institute for the Science of Light in Erlangen. He then moved back to his home country and has held a faculty position since 2017. With about 18 years of research experience within the field of Quantum Computing, Sadegh is probably most recognized for his pioneering works on Macroscopic Quantumness and algorithmic cooling, including finding the cooling limit of Heat-bath Algorithmic Cooling (HBAC) techniques which was an open problem for a decade, and for inventing the Blind HBAC technique, which is the optimal and practical HBAC technique. Sedighe Raeisi has a background in high-energy physics, nonlinear dynamics and chaotic systems. She holds a Ph.D. from Ferdowsi University of Mashhad where she also worked for 2 years as a lecturer after graduation. Her areas of expertise include machine learning and deep learning with special focus on Natural Language Processing (NLP), Machine Vision, Graph Neural networks and time series forecasting. She is currently working as a Data scientist in the Research and Development division of Iran’s largest telecommunications company.

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