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Machine learning for physicists : a hands-on approach
Author
Title
Machine learning for physicists : a hands-on approach / Sadegh Raeisi, Sedighe Raeisi.
ISBN
9780750349574
9780750349567
9780750349550
9780750349581
Publication
Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2023]
Physical Description
1 online resource : illustrations (some color).
Local Notes
Access is available to the Yale community.
Notes
"Version: 20231101"--Title page verso.
Access and use
Access restricted by licensing agreement.
Biographical / Historical Note
Sadegh Raeisi has a background in quantum computing and quantum information science. He completed his MSc at the University of Calgary and his PhD 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 PhD from Ferdowsi University of Mashhad where she also worked for two 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.
Summary
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.
Variant and related titles
IOP ebooks.
Other formats
Also available in print.
Print version:
Format
Books / Online
Language
English
Added to Catalog
December 13, 2023
Series
[IOP release $release]
IOP ebooks. [2023 collection]
Bibliography
Includes bibliographical references.
Audience
Advanced undergraduate and graduate students in the physical sciences and their lecturers.
Contents
1. Preliminaries
1.1. Python
1.2. GitHub library
1.3. Datasets
2. Introduction
2.1. What is machine learning?
2.2. Applications of machine learning
2.3. Different types of machine learning
2.4. Structure of the book
part I. Supervised learning. 3. Supervised learning
3.1. Definitions, notations, and problem statement
3.2. Ingredients of supervised learning
3.3. Model evaluation and model selection
3.4. Summary
3.5. Questions
4. Neural networks
4.1. Introduction to neural networks
4.2. Training neural networks
4.3. Libraries for working with neural networks
4.4. Summary
5. Special neural networks
5.1. Convolutional neural network (CNN)
5.2. Time-series and recurrent neural networks (RNNs)
5.3. Graph neural network
5.4. Summary
part II. Unsupervised learning. 6. Unsupervised learning
6.1. Clustering
6.2. Anomaly detection
6.3. Dimensionality reduction
6.4. Summary
7. Generative models
7.1. Maximum likelihood estimation
7.2. Restricted Boltzmann machines
7.3. Generative adversarial networks (GAN)
7.4. Summary.
Subjects
Also listed under
Institute of Physics (Great Britain), publisher.
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