The Open Source
Innovation Spring 2016

Qualité logicielle | IoT | Cloud | Big Data | Systèmes d'information

Avril-juin 2016, Paris - Villetaneuse - Neuilly

Présidence : Roberto Di Cosmo, vice-président du GTLL, directeur de l’Irill, directeur de recherche Inria, Professeur à l’Université Paris-Diderot

Organisé par : logo Systematic & logo Initiative de Recherche et Innovation sur le Logiciel Libre

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Scikit-Learn Day

15 Juin 2016 — Scikit-Learn Day

09h-18h

ESILV, Paris Big Data

Résumé

The Scikit-Learn Day 2016: for enthousiasts, contributors and startup founders!

Direction de programme

Programme dirigé par Alexandre Gramfort (Telecom ParisTech) et Gaël Varoquaux (Inria)

Description détaillée

The Scikit-Learn Day is organised by the Scikit-Learn core development team, as part of PyData Paris 2016. Beware the number of seats available is limited ! For PyData Paris, you can buy your tickets separately.

Programme résumé

  • 08:30: Accueil
  • 09:00: Scikit-Learn, the State of the Union

    Gaël Varoquaux, Inria

  • 09:00: I. NEW RELEASES, NEW GEMS
  • 09:20: Anomaly and Novelty Detection with scikit-learn

    Alexandre Gramfort, Telecom ParisTech

  • 09:50: Gradient Boosting: a practical guide

    Olivier Grisel, Inria

  • 10:20: Pause
  • 10:49: II. LEARNING WITH SCIKIT-LEARN
  • 10:50: Se former par l'open source

    Tom Dupré la Tour

  • 11:20: Bénéfices de scikit-learn pour l'enseignant-chercheur en machine learning

    Chloé-Agathe Azencott, Mines ParisTech

  • 11:40: Enseigner le machine learning avec Scikit-Learn: L'expérience de Télécom ParisTech

    Alexandre Gramfort, Telecom ParisTech

  • 12:00: Déjeuner networking
  • 13:29: III. ANIMER LA COMMUNAUTE ET CONTRIBUER
  • 13:30: Comment contribuer à Scikit-Learn ?

    Loic Esteve, Inria

  • 14:00: Software Carpentry: teaching as a community effort

    Bartosz Telenczuk, CNRS Gif-sur-Yvette

  • 14:15: Rapid Analytics and Model Prototyping (RAMP): a collaborative challenge

    Balász Kégl, CNRS-In2p3

  • 14:30: Pause
  • 15:14: IV. SUCCESS STORIES & APPLICATIONS
  • 15:15: Contribution d'un industriel : Scikit Learn à l'échelle

    Jean-Paul Smets, Nexedi

  • 15:30: Import sklearn : practical way we use it, and future thoughts

    Florian Douetteau, Dataiku

  • 16:00: Software Engineers, the new Data Scientists

    Christophe Bourguignat, Zelros

  • 16:30: Malware Classification with scikit-learn & open questions (QA, governance)

    Fabien Mangeant, Vincent Feuillard, Pierre Benjamin, Airbus

  • 16:45: OPEN DISCUSSION

Lieu

ESILV

Ville: Paris


Programme détaillé

  • 08:30 - Accueil
  • 09:00 - Scikit-Learn, the State of the Union

    Gaël Varoquaux, Inria

  • 09:00 - I. NEW RELEASES, NEW GEMS
  • 09:20 - Anomaly and Novelty Detection with scikit-learn

    Alexandre Gramfort, Telecom ParisTech

  • 09:50 - Gradient Boosting: a practical guide

    Olivier Grisel, Inria

  • 10:20 - Pause
  • 10:49 - II. LEARNING WITH SCIKIT-LEARN
  • 10:50 - Se former par l'open source

    Tom Dupré la Tour

    Tout ce que Scikit-Learn peut apporter en parallèle d'un cursus académique : contribution à un projet open source, expérience collaborative, accélération de l'apprentissage par la revue par les pairs, etc.

  • 11:20 - Bénéfices de scikit-learn pour l'enseignant-chercheur en machine learning

    Chloé-Agathe Azencott, Mines ParisTech

    Chargée de recherche au Centre de bioinformatique de Mines ParisTech et enseignant le machine learning et la bioinformatique, Chloé-Agathe Azencott partagera son expérience d'utilisatrice de scikit-learn.

  • 11:40 - Enseigner le machine learning avec Scikit-Learn: L'expérience de Télécom ParisTech

    Alexandre Gramfort, Telecom ParisTech

  • 12:00 - Déjeuner networking
  • 13:29 - III. ANIMER LA COMMUNAUTE ET CONTRIBUER
  • 13:30 - Comment contribuer à Scikit-Learn ?

    Loic Esteve, Inria

  • 14:00 - Software Carpentry: teaching as a community effort

    Bartosz Telenczuk, CNRS Gif-sur-Yvette

    Since 1998, Software Carpentry has been teaching researchers in science, engineering, medicine, and related disciplines the computing skills they need to get more done in less time and with less pain. We'll be talking about the teaching (what and how) and the community effort to share the work and keep the workshops going.

  • 14:15 - Rapid Analytics and Model Prototyping (RAMP): a collaborative challenge

    Balász Kégl, CNRS-In2p3

    We will be describing the RAMP, a rapid data challenge format and tool we developed at the Paris-Saclay Center for Data Science. A RAMP is, at the same time, a collaborative prototyping challenge, a training session for novice data scientist, a networking opportunity, and a social science observatory.

  • 14:30 - Pause
  • 15:14 - IV. SUCCESS STORIES & APPLICATIONS
  • 15:15 - Contribution d'un industriel : Scikit Learn à l'échelle

    Jean-Paul Smets, Nexedi

  • 15:30 - Import sklearn : practical way we use it, and future thoughts

    Florian Douetteau, Dataiku

    In this presentation, we  will  drill through several practical use cases for Scikit-Learn, in logistics or customer analytics. We will present full workflows that combine Python / R / Hadoop and Scikit-Learn to achieve practical business goals, and present some thoughts about how the ML ecosystem is moving forward  to better support those

  • 16:00 - Software Engineers, the new Data Scientists

    Christophe Bourguignat, Zelros

    Cards are reshuffled, as machine learning usages are growing, specialised software libraries are becoming a commodity, and MOOCs bring knowledge to the mass. In this talk, we will see how modern applications need building blocks like scikit-learn, and a new breed of software engineers proficient in machine learning.

  • 16:30 - Malware Classification with scikit-learn & open questions (QA, governance)

    Fabien Mangeant, Vincent Feuillard, Pierre Benjamin, Airbus

    Using Scikit-Learn's text-mining functionalities to build malware classification algorithms. Further questions about the project's maintenance, contribution, roadmap governance will be raised.

  • 16:45 - OPEN DISCUSSION

Organisateurs et/ou sponsors

Organisateurs (2016)

Co-organisateurs et sponsors (2016)

Ils parlent de l'OSIS (2016)

Soutiens du Pôle Systematic