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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Deep Learning on scientific data
Année
2014
Starting
01/10/2014
État
Open
Sujet
Learning to discover: supervised discrimination and unsupervised representation learning with applications in particle physics
Thesis advisor
KÉGL Balázs
Co-advisors
Laboratory
EXT
Collaborations
Laboratoire de l'Accélérateur Linéaire
Abstract
Today, machine learning methods are routinely used in high-energy particle physics to accelerate the discovery of new phenomena. Typically, standard classification algorithms are used for signal/background separation both in the online selection (trigger) step, and in the offline analysis. The first goal of this thesis to answer some of the theoretical questions raised by these unorthodox machine learning applications, and to design new algorithms that improve the analyses. In the second theme we propose to go beyond the standard setup of ``manual'' feature extraction followed by classification and to investigate the applicability of recently developed techniques on unsupervised representation learning. Both themes are motivated by concrete particle and astroparticle physics experiments, the future International Linear Collider (ILC), and the Pierre Auger Experiment (Auger). Data provided by these experiments will be a natural testbed for methodolgies developed in the thesis.
Context
Objectives
Work program
Extra information
Prerequisite
Détails
Télécharger learningToDiscover.pdf
Expected funding
Institutional funding
Status of funding
Expected
Candidates
Mehdi Cherti
Utilisateur
balazs.kegl
Créé
Dimanche 18 mai 2014 08:32:16 CEST
dernière modif.
Dimanche 18 mai 2014 08:54:46 CEST

Fichiers joints

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learningToDiscover.pdf 18 mai 2014 08:542630102.63 Kb


Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr