Description

The field of big data processing is currently percieved as a crucial tool for many scientific disciplines, including signal processing, finance, biology... Significant progress has been achieved in the development of a wide variety of algorithms for solving many problems in big data processing. This fast development was, however, contrasted by a lack of theoretical tools enabling performance assessment of these algorithms.

3-days course on big data processing

To allow for comparison between existing methods and to obtain better designs, theoretical analysis of alogrithms was needed. Recent works were able to make a compelling connection between theory of large dimensional random matrices and big data processing. This 3-day course will go beyond providing an overview of tools; from this theory to promoting their use for addressing problems in big data processing.

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Participant are encouraged to attend the lecture about “When random matrix Approach meet Machine learning on Thursday 25^th January (12:30 – 13:30)