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A data scientific perspective on arithmetic

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Number theory, also known as arithmetic, is the study of integers. Throughout the subject’s history, various conceptual frameworks have been leveraged to make progress. Naturally, this includes concepts from algebra and analysis but perhaps more surprising in the context of “pure” mathematics is the role played by data. Before computers, the likes of Gauss, Legendre, and Chebyshev were formulating arithmetic conjectures based on large samples. More recently, the famous Birch and Swinnerton-Dyer conjecture emerged from computational experimentation. In this talk, we will explore ways in which one can introduce contemporary data scientific techniques to the study of arithmetic, with a view towards solving old conjectures and formulating new ones.

This talk is part of the ML@CL Ad-hoc Seminar Series series.

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