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

✦ Table of Contents

PRELIMS.pdf
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To Mom and Dad, Farah and HadiFor having our backs every step of the way and for being there for us all these years.
References
Acknowledgements
Author biographies
Sadegh Raeisi
Sedighe Raeisi
CH001.pdf
Chapter Preliminaries
1.1 Python
1.2 GitHub library
1.3 Datasets
References
CH002.pdf
Chapter 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
CH003.pdf
Chapter 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
CH004.pdf
Chapter 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
CH005.pdf
Chapter 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
CH006.pdf
Chapter 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
CH007.pdf
Chapter Generative models
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Example: Gaussian data
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