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Machine Learning for Physics
Introduction to Data Science
Slides
Jupyter Notebooks and Numerical Python
Handling Data
Visualizing Data
Finding Structure in Data
Measuring and Reducing Dimensionality
Adapting Linear Methods to Non-Linear Data and Kernel Functions
Homework 01: Introduction to Data Science
Probability Theory and Density Estimation
Slides
Probability Theory
Important Probability Distributions
Estimating Probability Density from Data
Homework 02: Probability Theory and Density Estimation
Bayesian Statistics I
Slides
Statistics
Bayesian Statistics
Markov Chain Monte Carlo in Practice
Stochastic Processes and Markov-Chain Theory
Homework 03: Bayesian Statistics and Markov Chains
Bayesian Statistics II
Slides
Bayesian Model Selection
Variational Inference
Optimization
Cross Validation
Homework 04: Metropolis-Hastings and Cross Validation
Introduction to Artificial Intelligence
Slides
Supervised Learning
Artificial Intelligence and Machine Learning
Artificial Neural Networks
Deep Learning
Homework 05: Artificial Neural Networks
Convolutional and Recurrent Neural Networks
Slides
Convolutional and Recurrent Neural Networks
Homework 06: Forecasting Projectile Motion with Recurrent Neural Networks
Geometric Deep Learning and Graph Neural Networks
Slides
Geometric Deep Learning and Graph Neural Networks
Attention Mechanism and Transformers
Slides
Attention
Transformers
Vision Transformer
Project 01
Higgs Boson Decaying to Tau Leptons
Searching for Exotic Particles
Galaxy Zoo
Nuclear Geometry and Characterization of the Quark Gluon Plasma
Aberrated Image Recovery of Ultracold Atoms
Dark Energy Survey
Detection of Gravitational Waves
Generative Modeling and Simulation-Based Inference
Slides
Generative Modeling
Autoencoders
Variational AutoEncoders
Generative Adversarial Networks
Diffusion Models
Normalizing Flows
Simulation Based Inference
Reinforcement Learning
Slides
Reinforcement Learning
Homework 07: Reinforcement Learning: Implementing a Deep Q-Network
AI Explainablility and Uncertainty Quantification
Slides
AI Explainability and Uncertainty Quantification
Homework 08: Detecting Distribution Shift on MNIST using Bayesian Neural Networks
Unsupervised Learning and Anomaly Detection
Slides
Unsupervised Learning and Anomaly Detection
Physics Informed Neural Networks
Slides
Physics Informed Neural Networks
Solving the Time Dependent Schrodinger Equation with Physics-Informed Deep Learning
Introduction to Symbolic Regression
Project 02
Anisotropy in the Quark Gluon Plasma
Aberrated Image Recovery of Ultracold Atoms
Beam Angle Optimization for Radiative Cancer Therapy
Precision Neutron Counting with Tail Pulse Pileup Rejection
Fluid Dynamics of a Bent Pipe
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
.pdf
Cross Validation
Contents
Acknowledgments
Cross Validation
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Acknowledgments
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Initial version: Mark Neubauer
© Copyright 2026
Contents
Acknowledgments
