Aim of the Workshop

The joint availability of computational power and huge datasets has considerably changed the landscape of Artificial Intelligence. In many fields, applications (self-driving cars, cybersecurity, e-health…) that seemed out of reach in the past are now closer to becoming a reality. Recent advances in Machine Learning, the key component of AI,  show the growing maturity of algorithms that are now able to handle an increasing number of new tasks. However, simple adversarial attacks can still easily defeat a learning algorithm and the potentially massive deployment of AI tools in various environments raises many new concerns.  Additionally to scalability and versatility of algorithms, awareness of drifting or fake data, privacy, interpretability, accountability are now all features that a learning and decision system should take into account. This workshop aims at providing a non-exhaustive overview of  recent approaches developed to cope with these stimulating challenges of AI. This first instance of the International Workshop on Machine Learning and AI will serve as a forum for academics and practitioners working on both theoretical and the practical aspects of learning systems for AI.


About us

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.