Cooperation on Scikit-Learn with New York University

Published on in event-world

The Machine Learning for Big Data Chair is funding a 3-months-and-half visit for Nicolas Goix at New York University Center for Data Science, from May 16th to September 1st. It aims at providing development and research to the open-source machine learning library scikit-learn, under the supervision of Dr. Andreas Müller, researcher, core developer and maintainer of scikit-learn.

Nicolas Goix is a 3rd year PhD student, member of the Chair, working on machine learning methods for anomaly detection. A first collaboration funded by Paris-Saclay Center for Data Science has yielded the implementation of Isolation Forest on scikit-learn. As a continuation, Nicolas Goix will work on improving existing graphical models and on adding state-of-the-art Anomaly Detection algorithms to scikit-learn.

Scikit-learn is an open-source Python library which provides well-established machine learning methods. It extends the general-purposed programming language Python with machine learning operations, implementing many established algorithms, both supervised and unsupervised, while keeping an easy-to-use interface. Because of the large number of developers, emphasis is put on keeping the project maintainable, mainly by avoiding duplicating code. Pr. Alexandre Gramfort, member of the Machine Learning for Big Data Chair, is a founder and a core contributor of scikit-learn.