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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 and Machine Learning
Deep Learning and Generative Modeling
Convolutional and Recurrent Neural Networks
Project 01
Geometric Deep Learning and Graph Neural Networks
- Slides
- Geometric Deep Learning and Graph Neural Networks
Attention Mechanism and Transformers
- Slides
- Attention Mechanism and Transformers
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

Reinforcement Learning

Contents

Acknowledgments

Reinforcement Learning#

Acknowledgments#

Initial version: Mark Neubauer
Author: Adam Paszke
From this notebook

© Copyright 2025

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Reinforcement Learning

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Homework 07: Reinforcement Learning: Implementing a Deep Q-Network

Contents

Acknowledgments

By Mark Neubauer

© Copyright 2024.