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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

Colab

.ipynb

Homework 03: K-means and Principle Component Analysis

Contents

Acknowledgments

Homework 03: K-means and Principle Component Analysis#

Acknowledgments#

Initial version: Mark Neubauer

© Copyright 2025

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Adapting Linear Methods to Non-Linear Data and Kernel Functions

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Probability Theory

Contents

Acknowledgments

By Mark Neubauer

© Copyright 2024.