TY  - BOOK
AU  - Raeisi,Sadegh
AU  - Raeisi,Sedighe
ED  - Institute of Physics (Great Britain),
TI  - Machine learning for physicists: a hands-on approach 
T2  - [IOP release $release]
SN  - 9780750349574
AV  - QC524b.R346 2023eb 
PY  - 2023///]
CY  - Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK)
PB  - IOP Publishing
KW  - Physics
KW  - Data processing
KW  - Machine learning
KW  - Artificial intelligence
KW  - bicssc
KW  - COMPUTERS / Artificial Intelligence / General
KW  - bisacsh
N1  - "Version: 20231101"--Title page verso; Includes bibliographical references; 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; Advanced undergraduate and graduate students in the physical sciences and their lecturers; Also available in print
N2  - 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
UR  - https://iopscience.iop.org/book/mono/978-0-7503-4957-4
ER  -