BU BÖLÜM HAKKINDA
What would a "theory" of machine learning tell us? In this episode Tom meets with the person who invented what is now the widely accepted definition of supervised machine learning: Turing Award recipient and Harvard Professor Leslie Valiant.
Leslie tells us how he got interested in the problem, his contribution, the evolution of machine learning theory over the decades, and his advice to new researchers.
ingilizce
Amerika Birleşik Devletleri
BU BÖLÜMDE
TRANSKRİPT 🔗
Are you the producer of this podcast?
Add a podcast transcript
Need Audio-to-Text?
Transcribe with Listen411 in Just 60 Seconds
SON BÖLÜMLERİ ARA
Machine Learning: How Did We Get Here? için geçmiş bölümleri ara.
BU PODCAST'IN DİĞER BÖLÜMLERİ
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 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 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 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 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…
Feragatname: Bu sayfaya yerleştirilmiş podcast ve sanat eserleri, sahibinin mülkiyetinde olan ve Listen Notes, Inc.'e bağlı olmayan veya tarafından onaylanmayan Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University'e aittir.
DÜZENLE
Podcast veritabanını güncel tutmaya yardımcı olduğunuz için teşekkür ederiz.
