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 “Machine Learning for Big Data” Chair  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 Télécom ParisTech. Created in September 2013 with the support of the Fondation Mines-Télécom, the Chair is funded by five companies: Safran, PSA Group, Criteo, BNP Paribas, and Valeo who joined the Chair in June 2017. The Chair is supported by the mathematician Stephan Clémençon, Professor in the Image, Data, Signal department of Télécom ParisTech.

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NIPS Conference 2018 in Montreal

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Cooperation on Scikit-Learn with New York University

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