Machine Learning for Physicists: A hands-on approach

✍ Scribed by Sadegh Raeisi; Sedighe Raeisi

• Publisher: IOP Publishing
• Year: 2023
• Tongue: English
• Leaves: 234
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Cover
Title
Copyright
Contents
Preface
Acknowledgements
Author biographies
1 Preliminaries
1.1 Python
1.2 GitHub library
1.3 Datasets
References
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
References
Part I Supervised learning
3 Supervised learning
3.1 Definitions, notations, and problem statement
3.2 Ingredients of supervised learning
3.2.1 Data
3.2.2 Supervised learning models
3.2.3 Loss functions
3.2.4 Optimization techniques
3.3 Model evaluation and model selection
3.3.1 A good fit versus a good predictor: in-sample and out-sample errors
3.3.2 Over-fitting and under-fitting
3.3.3 Bias and variance trade-off
3.3.4 Model tuning
3.3.5 Metrics
3.3.6 Cross-validation
3.4 Summary
3.5 Questions
References
4 Neural networks
4.1 Introduction to neural networks
4.1.1 What is a neural network?
4.1.2 Notation
4.1.3 Intuition
4.1.4 Universality of neural networks
4.2 Training neural networks
4.3 Libraries for working with neural networks
4.4 Summary
References
5 Special neural networks
5.1 Convolutional neural network (CNN)
5.1.1 Convolution
5.2 Time-series and recurrent neural networks (RNNs)
5.2.1 Time series analysis
5.2.2 Classical models in time series forecasting
5.2.3 Recurrent neural network
5.2.4 Implementation of RNN
5.3 Graph neural network
5.3.1 Brief review of graphs
5.3.2 Different types of problems
5.3.3 GCN and embeddings
5.4 Summary
References
Part II Unsupervised learning
6 Unsupervised learning
6.1 Clustering
6.1.1 Mathematical definition
6.1.2 Different types of clustering
6.1.3 Evaluation of a clustering algorithm
6.1.4 Comparison of different clustering techniques
6.1.5 Implementation of clustering algorithms
6.2 Anomaly detection
6.2.1 Algorithms for anomaly detection
6.3 Dimensionality reduction
6.3.1 Auto-encoders
6.4 Summary
References
7 Generative models
7.1 Maximum likelihood estimation
7.2 Restricted Boltzmann machines
7.2.1 Training an RBM: contrastive divergence algorithm (CDA)
7.3 Generative adversarial networks (GAN)
7.4 Summary
References