BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
TZID:Europe/Paris
X-WR-TIMEZONE:Europe/Paris
BEGIN:VEVENT
UID:6569@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20210125T000000
DTEND;TZID=Europe/Paris:20210129T000000
DTSTAMP:20241221T195821Z
URL:https://www.i2m.univ-amu.fr/evenements/mathematics-signal-processing-a
 nd-learning/
SUMMARY:School (CIRM\, Luminy\, Marseille): Mathematics\, Signal Processing
  and Learning
DESCRIPTION:School: \n\n\n\n\n\n\n\n\n Time Schedule\nAbstracts\n\n\n\n\n\n
  Participants \n\n\n\n\n\n Videos \n\n\n\n\n\n BOOKLET\nfor Posters \n\n\n
 \n\n\n Links to\npratical sessions \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHYBRI
 D RESEARCH SCHOOL\nECOLE DE RECHERCHE\n​\nMathematics\, Signal Processin
 g and Learning\nMathématiques\, traitement du signal et apprentissage​\
 n​\n25 - 29 January 2021\n\nOrganizing Committee\nComité d'organisation
 \nSandrine Anthoine (CNRS - Aix-Marseille Université)\nCaroline Chaux (CN
 RS - Aix-Marseille Université)\nHachem Kadri (Aix-Marseille Université)\
 nFrédéric Richard (Aix-Marseille Université)\n\nScientific Committee \n
 Comité scientifique\nFrancis Bach (INRIA Paris)\nRichard Baraniuk (Rice U
 niversity)\nGabriel Peyré (CNRS - ENS Paris)\n\n\n\n\n\n  \n\n\n\n\n\n\n\
 n\n\n\nDescription\n\n\n\n\n\n\n\n\nThis week will consist of a doctoral s
 chool on mathematics and learning with an emphasis on signal and image pro
 cessing. The main topic will be the basics of learning\, plus more advance
 d classes on reinforcement learning and deep learning for example\, as wel
 l as classes on signal processing and optimization in machine learning. Mo
 st of the lectures will adopt a mathematical view of machine learning and 
 will feature practical sessions (for example in Python). Finally\, the par
 ticipants will also have the opportunity to present their work in poster o
 r short oral sessions.\n\n\n\nCette école de recherche portera sur les m
 athématiques et l’apprentissage\, avec une coloration traitement du si
 gnal et de l’image. On y abordera des notions de bases de l’apprentiss
 age et du traitement du signal\, mais aussi des notions plus avancées d
 ’optimisation\, d’apprentissage par renforcement et d’apprentissage 
 profond. On veillera à ce que des aspects pratiques y soient associés 
 (via des travaux pratiques sous Python). Enfin\, on laissera l’opportuni
 té aux participants de présenter leur travaux au travers de posters ou
  de présentations courtes.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeakers\n\n\n
  	Basics in machine learning: \n\nMarianne Clausel (Université de Lorra
 ine)\nLecture 1    Lecture 2\nPractical session 1   Practical session
  1\n\n 	Signal processing:\n\n​Nicolas Vayatis (ENS Paris-Saclay)\nLaure
 nt Oudre (ENS Paris-Saclay)\ntutorial - part 1      tutorial - part 2\n
 ​\n\n 	Optimization:\n\n​​Nelly Pustelnik (CNRS\, ENS Lyon)\n​Lect
 ure 1    Lecture 2     Lecture 3    Lecture 4\n\n\n\n\n\n\n 	R
 einforcement learning:\n\nAllesandro Lazaric (Facebook AI Research)\n​Le
 cture 1    Lecture 2     Lecture 3    Lecture 4\n​\n\n 	One s
 ignal processing view on deep learning:    \n\nEdouard Oyallon (LIP6\, C
 NRS)\nLecture 1    Lecture 2\n\n 	Teasing poster: mathematics\, signal p
 rocessing and learning\n\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n
CATEGORIES:École ou Master class
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20201025T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
END:VTIMEZONE
END:VCALENDAR