SOBRE ESTE EPISÓDIO
Tom sits down with Michael I. Jordan, Director of Rearch at Inria and Professor Emeritus of the Departments of EECS and Statistics, University of California, Berkeley. Michael has been a major contributor to machine learning, especially at the intersection of statistics and machine learning.
Michael discusses his research trajectory, including how it has been inspired by ideas from control theory, statistics, and most recently economics.
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Estados Unidos da América
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OUTROS EPISÓDIOS NESTE PODCAST
Tom interviews Rich Sutton, Research Scientist at Keen Technologies, Professor of Computing Science at the University of Alberta and co-winner of the 2024 ACM Turing Award for his foundational research on reinforcement learning.
Rich discusses why the common framing of machine learning as 'supervis…
Tom discusses the chaotic evolution of the field of machine learning with Tom Dietterich, Distinguished Professor Emeritus at Oregon State University.
Tom has made numerous research contributions to the field, and has served in professional roles from Executive Editor of the journal Machine Learnin…
Tom speaks with Ross Quinlan, whose algorithms C4.5 and ID3 helped establish decision trees as one of the most popular approaches in machine learning, and who founded RuleQuest Research, which accelerated the commercial adoption of machine learning.
Ross (published as "JR Quinlan") describes a sabb…
Tom sits down with Yann LeCun, the Jacob T. Schwartz Professor of Computer Science at NYU, and Executive Chairman of Advanced Machine Intelligence Labs.
Yann is co-winner of the 2018 ACM Turing Award for his research in neural network learning. Yann takes us from his days as a postdoc working with …
Tom sits down with Geoffrey Hinton, University Professor Emeritus at the University of Toronto, and co-winner of the ACM Turing Award and of the 2024 Nobel Prize in Physics.
Geoffrey explains how he got into the field, from his days as an aspiring carpenter to his conversion to a neural network res…
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EDITAR
Obrigado por ajudar a manter a base de dados dos podcasts atualizada.
