Due to the actual sanitary situation, we are discussing how to maintain the school for this year. More news to come...
[The school was planned in the region of Marseille from Monday November 30 to Friday December 4 2020 (orginal period from Monday 11 May to Friday 15 May 2020 was cancelled due to pandemy).]
The seventh edition of the IN2P3 School Of Statistics will give an overview of the concepts and tools used in particle physics, astroparticle physics and cosmology when probabilities and statistics come to play.
This school is targeted towards PhD students and senior physicists, aiming at extending their knowledge and skills in the field of statistical tools and frameworks developed for their fields.
The school combines lectures and hands-on sessions. The lectures are subdivided into three sections:
- a reminder of the fundamental concepts used in Probabilities, Statistics and Hypothesis testing applied to physics analysis;
- the presentation of the concepts and basics of most popular multivariate techniques;
- and a section dedicated to actual multivariate tools and machine learning.
All lectures will be given in English. The School Of Statistics is supported by CNRS/IN2P3.
Main support comes from CNRS/IN2P3 however SOS 2020 get also substantial financial support from ENIGMASS, OCEVU and P2IO labex, and AMVA4NewPhysics ITN, as well as administrative and technical support from the CPPM laboratory.
Johan Bregeon (LPSC, Grenoble), Nicolas Chanon (IP2I Lyon), Yann Coadou (CPPM, Marseille), Guillaume Mention (IRFU-DPhP, Saclay), Sabine Crépé-Renaudin (LPSC, Grenoble) - chair-, Laurent Derome (LPSC, Grenoble), Julien Donini (LPC, Clermont), Éric Chabert (IPHC, Strasbourg), David Rousseau (IJClab, Orsay)
Angélique Pèpe (CPPM, Marseille).
- Basic concepts of statistics: Romain Madar (LPC, Clermont-Ferrand)
- Classical interval estimation, limits, systematics and beyond: Glen Cowan (Royal Holloway, UK)
- Introduction to Machine Learning: Vincent Barra (LIMOS, Clermont-Ferrand)
- Boosted Decision Trees: Yann Coadou (CPPM, Marseille)
- Introduction to Deep Learning: Michael Kagan (SLAC, USA)
- Deep learning at colliders: Jean-Roch Vlimant (CalTech, USA)
- Multitask learning for astroparticle physics: Thomas Vuillaume (LAPP, Annecy)
- Statistics: Guillaume Mention (CEA/IRFU, Saclay)
- Machine Learning tools: David Rousseau (IJCLab, Orsay)
- Advanced Machine Learning: Jean-Roch Vlimant (CalTech, USA)
Registration: opening of registration will be announced later