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Thursday, May 30 • 11:30am - 12:15pm
Deliberations on Scientific and Methodological Aspects of Machine Learning

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Many diverse fields, such as applied mathematics, statistics, machine learning, data mining, econometrics, bioinformatics etc. are concerned with estimation of data-analytic models. More recently, due to the abundance of data and cheap computing power, machine learning (ML) algorithms have become very popular in various applications, even though many such algorithms are heuristics vaguely motivated by biological (as opposed to mathematical) arguments. This disconnect (between mathematics and practical applications) may seem strange, given the deep intrinsic connection between mathematics, science and engineering. The purpose of my talk is to explain various reasons for the current disconnect, including (a) conceptual (philosophical) aspects; (b) technical (mathematical) aspects and (c) non-technical (social) aspects. In particular, my talk will elaborate on different interpretation of philosophical concepts (of deductive and inductive reasoning), in classical science, statistics and ML. 

Speakers
avatar for Vladimir Cherkassky, PhD

Vladimir Cherkassky, PhD

Professor, Electrical & Computer Eng., University of Minnesota
Vladimir Cherkassky is Professor of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. He received a PhD in Electrical and Computer Engineering from the University of Texas at Austin in 1985. 


Thursday May 30, 2019 11:30am - 12:15pm
(H) P1838 Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431

Attendees (57)