Originally planned to be organized in the region of Marseille (from Monday 11 May to Friday 15 May 2020) and cancelled due to the pandemic, the next SOS edition is finally organized fully remotely due to the current sanitary situation.
The school starts on Monday 18 January 2021 and ends on Friday 29 January 2021. The detailed agenda is now available. Note that all courses take place only in the morning. The school is free of charge.
This 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 into 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 the 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.
All sessions are recorded and posted on the agenda. Please note that they might not be viewable directly in your browser, in this case, right-Clic to download them ( the files are large, up to 500MB).
Johan Bregeon (LPSC, Grenoble), Nicolas Chanon (IP2I Lyon), Yann Coadou (CPPM, Marseille) - chair-, Guillaume Mention (IRFU-DPhP, Saclay), Sabine Crépé-Renaudin (LPSC, Grenoble), Laurent Derome (LPSC, Grenoble), Julien Donini (LPC, Clermont), Éric Chabert (IPHC, Strasbourg), David Rousseau (IJClab, Orsay)
Angélique Pèpe (CPPM, Marseille).
- Basic concepts of statistics: Julien Donini (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)
- Deep Learning: Jean-Roch Vlimant (CalTech, USA)