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Technological advances, the ubiquity of sensors and the boom of social networks come with a real data deluge, putting information sciences and technologies at the center of the big data valorisation process. The statistical processing of this huge amount of data brings together applied maths and computer science through a quickly expanding discipline: machine learning. The volume and variety of available data make traditional statistical methods ineffective. It is the purpose of machine learning to elaborate and study algorithms that enable machines to learn automatically from data and perform tasks in an efficient way.

It is the goal of the Chair “Machine Learning for Big Data” to produce methodological research tackling the challenges of the statistical analysis of big data and to liven up the higher education program in that field at Telecom ParisTech. Created in September 2013 with the support of the Fondation Telecom, and funded by four companies: Safran, PSA Group, Criteo and BNP Paribas, the Chair is supported by the mathematician Stéphan Clémençon, Professor in the department of Signal and Image Processing at Telecom ParisTech.

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Cooperation on Scikit-Learn with New York University

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.

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