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Deep learning for particle physicists
Overview
History of HEP and ML
Basic fitting
Universal approximators
Neural networks
Exercise 1: TensorFlow Playground
Regression in PyTorch
Exercise 2: Regression
Classification in PyTorch
Exercise 3: Classification
Minimization algorithms
Epochs and mini-batches
Exercise 4: Mini-batches and DataLoaders
Feature selection and “the kernel trick”
Under and overfitting
Regularization
Exercise 5: Regularization in the Playground
Hyperparameters and validation
Goodness of fit metrics
Main Project (2 hours)
Solutions (NO PEEKING!)
Beyond supervised learning: clustering
Autoencoders
Convolutional Neural Networks (CNNs)
Ragged data and Graph Neural Networks (GNNs)
Conclusion
Repository
Open issue
Index
