The experimental aspect: Machine Learning ingredients for precision Physics

Parametrized classifiers for EFT:

1. Introduzione a ML e deep learning
2. EFT weights morphing (ma forse queste cose le sanno gia'?)
3. Learning the likelihood ratio with a parametrized classifier for EFT
4. How to choose the best classifier

Transformer models for event interpretation

1. The transformer architecture
2. Using the transformer to analyze reconstructed particles and regress parton-level particles
3. How to use the correct losses for multi-target optimization and obtain the best multi-particle regression

The theoretical aspect: EFT simulation and fits

Advanced topics in SMEFT interpretation at LO

1. Quick SMEFT dim 6 recap
2. Constraints from EWPOs at LEP and the role of mW
3. State-of-the-art global fits and the role played by each ingredient: operator basis, predictions order, measurements, uncertainties, statistical methods.

Even more precision: higher-order SMEFT, in loops and in operator dimension

1. SMEFT at dim-6: RGE evolution, operator mixing and their impact in global fits
2. SMEFT at dim-8: motivation, technical challenges and case studies