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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
Normalizing Flows
Simulation Based Inference
Homework 04: Metropolis-Hastings and Cross Validation
Introduction to Artificial Intelligence and Machine Learning
Slides
Artificial Intelligence and Machine Learning
Optimization
Cross Validation
Supervised Learning
Artificial Neural Networks
Homework 05: Artificial Neural Networks
Deep Learning and Generative Modeling
Slides
Deep Learning
Generative Modeling
Convolutional and Recurrent Neural Networks
Slides
Convolutional and Recurrent Neural Networks
Homework 06: Forecasting Projectile Motion with Recurrent Neural Networks
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
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
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
Detection of Gravitational Waves
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
Generative Modeling
Contents
Acknowledgments
Generative Modeling
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Acknowledgments
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Initial version: Mark Neubauer
© Copyright 2024
Contents
Acknowledgments
