PHYS 503 :: Physics Illinois :: University of Illinois at Urbana-Champaign

Instrumentation Physics: Applications of Machine Learning

Readings and other sources

Unit 1 reading and reference material

• A Whirlwind Tour of Python, Jake VanderPlas: free pdf, notebooks online.
• Python Data Science Handbook
• Notebooks and numerical python
• Handling data
• Visualizing data
• Finding structure in data
• Measuring and reducing dimensionality
• Adapting linear methods to nonlinear problems
• Kernel Functions

Unit 2 reading and reference material

• Probability theory
• Estimate probability density from Data
• Statistical methods

Unit 3 reading and reference material

• Bayesian statistics
• Markov-chain Monte Carlo in practice
• Stochastic processes and Markov-chain theory
• Variational inference
• Optimization
• Frameworks for computational graphs and probabilistic programming
• An introduction to the theory of Markov processes mostly for physics students, C. Maes
• Bayesian model selection
• Learning in a probabilistic context

Unit 4 reading and reference material

• Supervised learning in Scikit Learn
• Cross validation

Unit 5 reading and reference material

• Neural networks
• Types of Neural Networks

Unit 6 reading and reference material

• Deep learning
• Relational inductive biases, deep learning, and graph networks
• DeepMind's GNN library
• Graph Neural Networks: A Review of Methods and Applications
• An Introduction to Deep Reinforcement Learning
• A Beginner’s Guide to Deep Reinforcement Learning

Unit 7 reading and reference material

• The Fast Machine Learning Laboratory
• Fast Inference of Deep Neural Networks in FPGAs for Particle Physics
• Real-time Reinforcement Learning

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