Speaker: Lenka Zdeborová, CNRS Researcher, Institut de Physique Théorique – CEA

Abstract: The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. The very purpose of physics is to provide understanding for empirically observed behaviour. From this point of view, the current success of machine learning provides a myriad of yet unexplained empirical observations that call for explanation. Physics functions by study of models that are simple enough to be studied and at the same time capture the salient features of the real system. In this lecture I will describe some of the history of statistical physics applied to machine learning and focus of the current hunt for suitable models, starting with a reflection on what should be the salient features they should capture, and methods to possibly solve them.

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