MLHEP'19 slides and notebooks

• Day 1:
  • Figures of Merit, overfitting (MLE vs MAP vs AP)
• Day 2:
  • Ensembles of models; bagging, boosting, random forest
  • Clustering
• Day 3:
  • Computing gradient by hand. Pytorch
  • Convolutional Neural Networks ;
  • Model Zoo:
• Day 4:
  • Bayesian 2
• Day 5
  • Learning to Pivot:
    • toy example 1:
    • toy example 2:
    • toy example 3:
    • SUSY exercise:
  • Language modeling
  • Tracking
• Day 6
  • Introductory example 1 :
  • Introductory example 2 :
  • Practice :
  • GANs 1
  • GANs 2
  • GANs 3
• Day 8
  • Black-Box:
    • ABO:
    • AVO:
  • NN optimisation
    • 1-scikit-search:
    • 2-skorch:
    • 3-bayesian_optimization:
    • 4-skorch_comet:
    • 5-skorch_skopt_comet:
  • Independence of NN classifier from a continuous parameter: