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Instrumentation Physics: Applications of Machine Learning

Introduction to Machine Learning and Data Science

Course Introduction
Visualizing & Finding Structure in Data
Dimensionality, Linearity and Kernel Functions

Probability and Statistics

Probability Theory
Kernel Density Estimation and Statistics

Bayesian Inference

Bayesian Statistics and Markov Chain Monte Carlo
Project 01
Stochastic Processes, Markov Chains & Variational Inference
Optimization and Model Selection

Supervised Learning & Cross Validation

Learning & Cross Validation

Artificial Neural Networks

Supervised Learning & Artificial Neural Networks

Deep Learning

Deep Neural Networks
Graph Neural Networks
- Slides
- Graph Neural Networks
Project 02
AI Explainablility and Uncertainty Quantification

Explainable AI and Accelerated Machine Learning

Unsupervised Learning and Anomaly Detection
- Slides
- Unsupervised Learning and Anomaly Detection

Accelerated Machine Learning and Inference

Accelerated Machine Learning and Inference
- Slides
- Accelerated Machine Learning and Inference

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Project 02

Contents

Select from one of the following projects:

Project 02#

Select from one of the following projects:#

Higgs Boson Decaying to Tau Leptons
Searching for Exotic Particles
Galaxy Zoo
Aberrated Image Recovery of Ultracold Atoms
Detection of Gravitational Waves
Anisotropy in the Quark-Gluon Plasma
Beam Angle Optimization for Radiation Therapy
Precision Neutron Counting with Tail Pulse Pileup Rejection
Fluid Dynamics of a Bent Pipe
Galaxy Morphology Classification with the Sloan Digital Sky Survey
Black Hole Parameter Inference from EHT Observations
Climate Model Neural Network Prediction
Modeling Fluid Flow Past a Cylinder

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Graph Neural Networks

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Higgs Boson Decaying to Tau Leptons

Contents

Select from one of the following projects:

By Mark Neubauer

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