The sixth edition of the IN2P3 School Of Statistics gives an overview of the concepts and tools used in particle physics, astro-particle physics and cosmology when probabilities and statistics come to play.
This school is targeted towards PhD students and towards senior physicists, aiming at extending their knowledge and skills in the field of statistical tools and frameworks developed for their fields.
The school takes place in Les Océanides Residence at La Londe Les Maures from Monday 28 May to Friday 1 June 2018.
The school combines lectures and hands-on sessions. The lectures are subdivided into three parts: a part reminding the fundamental concepts used in Probabilities, Statistics and Hypothesis testing applied to physics analysis; a part focusing on the presentation of the concepts and basics of most popular multivariate techniques; and a part 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 2018 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 (LUPM, Montpellier), Nicolas Chanon (IPNL Lyon), Yann Coadou (CPPM, Marseille), Fabrice Couderc (IRFU-DPhP, Saclay), Sabine Crépé-Renaudin (LPSC, Grenoble), Laurent Derome (LPSC, Grenoble), Julien Donini (LPC, Clermont), Boris Hippolyte (IPHC, Strasbourg), David Rousseau (LAL Orsay).
Angélique Pepe (CPPM, Marseille).
- Basics concepts: Julien Donini
- Classical interval estimation, limits, CLs, systematics: Tommaso Dorigo
- Introduction to Machine learning: Vincent Barra
- Boosted decision trees, TMVA: Yann Coadou
- Introduction to Deep Learning: Gilles Louppe
- Deep learning at colliders: Amir Farbin
- Optimal experiment design in astronomy: learning to adapt: Emille Ishida
- Hands on sessions
- Introduction to sklearn, BDT, gaussian process, generative model, etc: Gilles Louppe
- Deep learning: Amir Farbin