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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
Recurrent and Convolutional Neural Networks
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
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?

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Project 01

Contents

Select from one of the following projects:

Project 01#

Select from one of the following projects:#

Higgs Boson Decaying to Tau Leptons
Searching for Exotic Particles
Galaxy Zoo
Nuclear Geometry and Characterization of the Quark Gluon Plasma
Dark Energy Survey
Aberrated Image Recovery of Ultracold Atoms
Detection of Gravitational Waves
Climate Model Neural Network Prediction
Black Hole Parameter Inference from EHT Observations
Galaxy Morphology Classification with the Sloan Digital Sky Survey

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Vision Transformer

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Higgs Boson Decaying to Tau Leptons

Contents

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By Mark Neubauer

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