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Machine Learning and the Physical Sciences

About the course

Course setting
Teachers
Textbooks

Introduction

Introduction

Mathematical and statistical basis with essential computational elements

1. Chapter 1: Linear Algebra for Machine Learning
2. Chapter 2: Elements of Probability Theory and Statistics

Classical methods

3. Chapter 3: Linear Regression
4. Chapter 4: Optimization
5. Chapter 5: Logistic Regression
6. Chapter 6: Support Vector Machines
7. Chapter 7: Ensemble methods; from simple decision trees to Gradient Boosting

Deep Learning

8. Chapter 8: Neural Networks
9. Chapter 9: Neural Networks for Differential Equations
10. Chapter 10: Convolutional Neural Networks
11. Chapter 11: Recurrent Neural Networks
12. Chapter 12: Autoencoders
13. Chapter 13: Transformers

Generative Methods

14. Chapter 14: Boltzmann Machines
15. Chapter 15: Variational Autoencoders
16. Chapter 16: Diffusion Models and Normalizing Flows
17. Chapter 17: Generative Adversarial Networks

Concluding remarks and perspectives

18. Chapter 18: Concluding remarks and perspectives

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By Morten Hjorth-Jensen

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