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Machine Learning for Physics

Introduction to Data Science
Probability Theory and Density Estimation
Bayesian Statistics I
Bayesian Statistics II
Introduction to Artificial Intelligence
Convolutional and Recurrent Neural Networks
Geometric Deep Learning and Graph Neural Networks
- Slides
- Geometric Deep Learning and Graph Neural Networks
Attention Mechanism and Transformers
Project 01
Generative Modeling and Simulation-Based Inference
Reinforcement Learning
AI Explainablility and Uncertainty Quantification
Unsupervised Learning and Anomaly Detection
- Slides
- Unsupervised Learning and Anomaly Detection
Physics Informed Neural Networks
Project 02
Learning from the Machines
- Slides
- Learning Physics from the Machines
Future of AI and Physics: What Lies Ahead?
- Slides
- Future of AI and Physics: What Lies Ahead?

Colab

.ipynb

Jupyter Notebooks and Numerical Python

Contents

Acknowledgments

Jupyter Notebooks and Numerical Python#

Acknowledgments#

Initial version: Mark Neubauer

© Copyright 2026

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Introduction to Data Science

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Handling Data

Contents

Acknowledgments

By Mark Neubauer

© Copyright 2024.