Name: Machine Learning
Start: 2019-05-20T11:00:00+02:00
End: 2019-05-22T16:00:00+02:00
Location: Camogli

Monday, 20 May
- 1
  
  Introduction
- 2
  
  Lecture 1 - Overview on ML/DL in HEP
  
  This lecture aims at giving a broad overview of main concepts in ML in the context of HEP applications.
  
  Speaker: Amir Farbin (University of Texas at Arlington)
  
  INFN-ML-Lecture-1.pdf
- 13:00
  
  Lunch break
  
  On your own
- 3
  
  Lecture 2 - Introduction to the ML landscape and methods
  
  An introduction to the (general, not HEP specific) ML landscape is given.
  
  Speaker: Daniele Bonacorsi (BO)
  
  Camogli2019__DBonacorsi_MLlandscape.pdf
- 16:00
  
  Coffee break
- 4
  
  Hands-on session
  
  github for the hands-on part
- 5
  
  Compute vision and applications
  
  Speaker: Dr Ferrari A. (Argo Vision)
  
  Dataset per training e test
  
  MiniGuida alla configurazione di OpenCV
  
  Slides - full video
  
  Slides - no video
- 6
  
  ML in the online data acquisition
  
  Speaker: Cristiano Fanelli (ISS)
  
  ML_online_data_acquisition-C.Fanelli.pdf
- 7
  
  ML for the EIC
  
  Speaker: Dmitry Romanov (Jefferson Lab)
  
  EIC ML.pptx
Tuesday, 21 May
- 8
  
  Lecture 3 - End-2-end projects discussion: regression and classification
  
  The attendees will be guided through an example of regression techniques.
  
  Speaker: Daniele Bonacorsi (BO)
  
  Camogli2019__DBonacorsi_e2eProjectClassification.pdf
  
  Camogli2019__DBonacorsi_e2eProjectRegression.pdf
- 10:30
  
  Coffee break
- 9
  
  Lecture 4 - Rapid walk-through of Deep Learning
  
  The attendees will be guided through concepts and methods in Deep Learning.
  
  Speaker: Amir Farbin (University of Texas at Arlington)
  
  INFL-ML-Lecture-2.pdf
- 12:30
  
  Lunch break
  
  On your own
- 10
  
  Hands-on session (cont'd)
- 15:30
  
  Coffee break
- 11
  
  Hands-on session (cont'd)
- 12
  
  Graph Network for High Energy Physics
  
  Speaker: Dr Jean-Roch Vlimant (Caltech)
  
  vlimant_CamoglieINFN_May19.pdf
- 13
  
  Improving Reaction Identification with ROOT TMVA
  
  Speaker: Derek Glazier (University of Edinburgh)
  
  ReactionID.pdf
Wednesday, 22 May
- 14
  
  Lecture 5 - Training models and Gradient Descent (bonus: Decision Trees)
  
  The attendees will be guided through what's under the hood in terms of training models. An additional lecture on Decision Trees is posted.
  
  Speaker: Daniele Bonacorsi (BO)
  
  Camogli2019__DBonacorsi_DecisionTrees.pdf
  
  Camogli2019__DBonacorsi_TrainingModels.pdf
- 10:30
  
  Coffee break
- 15
  
  Lecture 6 - Keras and/or Deep Learning in HEP
  
  The attendes opted to be given a guided walk-through of the basics of Keras as from keras.io. Alternative choice was a seminar on Deep Learning in HEP, whose slides are anyway posted.
  
  Speaker: Amir Farbin (University of Texas at Arlington)
  
  INFN-ML-Lecture-3.pdf
- 12:30
  
  Lunch break
  
  On your own
- 16
  
  Closeout
  
  The closeout includes speakers presenting references and post-course self-training suggestions, as well as a discussion of attendees' open projects.
  
  Post-course references

Machine Learning

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Camogli