ESILV, Paris Big Data
Programme dirigé par Alexandre Gramfort (Telecom ParisTech) et Gaël Varoquaux (Inria)
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.
Gaël Varoquaux, Inria
Alexandre Gramfort, Telecom ParisTech
Olivier Grisel, Inria
Tom Dupré la Tour
Chloé-Agathe Azencott, Mines ParisTech
Alexandre Gramfort, Telecom ParisTech
Loic Esteve, Inria
Bartosz Telenczuk, CNRS Gif-sur-Yvette
Balász Kégl, CNRS-In2p3
Jean-Paul Smets, Nexedi
Florian Douetteau, Dataiku
Christophe Bourguignat, Zelros
Fabien Mangeant, Vincent Feuillard, Pierre Benjamin, Airbus
Gaël Varoquaux, Inria
Alexandre Gramfort, Telecom ParisTech
Olivier Grisel, Inria
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.
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.
Alexandre Gramfort, Telecom ParisTech
Loic Esteve, Inria
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.
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.
Jean-Paul Smets, Nexedi
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
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.
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.