Objective One of the most significant recent developments in applied machine learning has been the resurgence of ``deep learning'', usually in the form of artificial neural networks. The empirical success of deep learning is stunning, and deep learning based systems have already led to breakthroughs in computer vision and speech recognition. In contrast, from the theoretical point of view, by and large, we do not understand why deep learning is at all possible, since most state ofthe art theoretical results show that deep learning is computationally hard.Bridging this gap is a great challenge since it involves proficiency in several theoretic fields (algorithms, complexity, and statistics) and at the same time requires a good understanding of real world practical problems and the ability to conduct applied research. We believe that a good theory must lead to better practical algorithms. It should also broaden the applicability of learning in general, and deep learning in particular, to new domains. Such a practically relevant theory may also lead to a fundamental paradigm shift in the way we currently analyze the complexity of algorithms.Previous works by the PI and his colleagues and students have provided novel ways to analyze the computational complexity of learning algorithms and understand the tradeoffs between data and computational time. In this proposal, in order to bridge the gap between theory and practice, I suggest a departure from worst-case analyses and the development of a more optimistic, data dependent, theory with ``grey'' components. Success will lead to a breakthrough in our understanding of learning at large with significant potential for impact on the field of machine learning and its applications. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-StG-2015 - ERC Starting Grant Call for proposal ERC-2015-STG See other projects for this call Funding Scheme ERC-STG - Starting Grant Host institution THE HEBREW UNIVERSITY OF JERUSALEM Net EU contribution € 1 342 500,00 Address EDMOND J SAFRA CAMPUS GIVAT RAM 91904 Jerusalem Israel See on map Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 342 500,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all THE HEBREW UNIVERSITY OF JERUSALEM Israel Net EU contribution € 1 342 500,00 Address EDMOND J SAFRA CAMPUS GIVAT RAM 91904 Jerusalem See on map Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 342 500,00