Welcome

• Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)

Basic concepts of statistics - Part I

• Julien Donini (UBP/LPC/IN2P3)

Basic concepts of statistics - Part II

• Julien Donini (UBP/LPC/IN2P3)

Classical interval estimation, limits systematics and beyond - Part I

• Glen Cowan (Royal Holloway)

Classical interval estimation, limits systematics and beyond - Part II

• Glen Cowan (Royal Holloway)

Basics of statistics: hands-on

• Guillaume MENTION (CEA Saclay)

The link to the notebook: https://colab.research.google.com/github/gmention-at-cea/sos2021/blob/master/SOS2021_practice_Basic_statistics.ipynb

Introduction to Machine Learning

• Vincent Barra (ISIMA)

Boosted Decision Trees

• Yann Coadou (CPPM Marseille)

Basics Machine Leaning: hands-on

• David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)

To prepare the hands on session : please try and run beforehand https://colab.research.google.com/github/dhrou/HEPMLtutorials/blob/master/HEPML_HandsOn_install_check.ipynb This is not the tutorial per se, but this exercises xgboost, lightgbm and tensor flow, this is especially useful to test standalone installation. The two notebooks we will use are in GitHub dhrou/HEPMLtutorials : HEPML_HandsOn_BDT.ipynb and HEPML_HandsOn_NN.ipynb This session will be 9 to 12 with a break 10:30 to 10:45. I will stop regularly to let people experiment by themselves.

Introduction to Deep Learning - Part I

• Michael Kagan (SLAC)

Introduction to Deep Learning - Part II

• Michael Kagan (SLAC)

Deep learning at colliders

• Jean-Roch Vlimant (California Institute of Technology)

Advanced Machine Learning: hands-on

• Jean-Roch Vlimant (California Institute of Technology )

Multitasks learning for astroparticle physics

• Thomas Vuillaume (LAPP, CNRS)

Introduction to Deep Learning - Part III extra time

• Michael Kagan (SLAC)

slides shown are the second part from the second talk on Tuesday

Farewell

• Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)