Monday June 16

• Lecture 1: Artificial Neural Networks 101
• Exercise session 1: introduction to the pytorch framework and implementation of a simple ANN

Tuesday June 17

• Lecture 2: Regularization and Training of an ANNs
• Exercise session 2: end-to-end design and training of an MLP to learn the dynamic of a mechanical system, Hamiltonian NNs

Wednesday June 18

• Lecture 3: Neural Network Models for Sparse Interactions: CNNs and Graph Neural Networks
• Exercise session 3: training a CNN to identify phase transitions on a 2D Ising Model

Thursday June 19

• Lecture 4: Unsupervised learning and Anomaly Detection with Auto Encoders
• Exercise session 4: representation of the latent space learnt by an AE, anomaly detection in high-energy physics

Friday June 20

• Lecture+exercise 5: Uncertainty quantification in ANNs, Bayesian NNs or recent trends in deep dearning
• Hackathon/final exam