Machine Learning: How Did We Get Here?
Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University
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From Philosophy to Machine Learning with Bruce Buchanan
Tom sits down with Bruce Buchanan, a PhD Philosopher turned machine learning researcher. Bruce produced a key milestone for machine learning in the 1970s by creating the first program that…
AI Agents to Model Human Cognition with John Laird
Tom chats with John Laird, who has spent the past 40 years trying to build an AI agent that accomplishes the full range of human cognitive abilities, beginning with his 1980s PhD research on the SOAR…
Machine Learning and Speech Recognition with Kai-Fu Lee
Tom meets with Kai-Fu Lee, a pioneer in using machine learning to significantly advance speech recognition.Kai-Fu, former president of Google China and now Chairman of Sinovation Ventures and CEO of…
Machine Learning meets Cognitive Neuroscience with Jay McClelland
What is the relationship between neural network approaches in machine learning, and real neural networks in the brain? Today's guest Jay McClelland is a cognitive scientist who has spent decades…
Learning Probabilistic Models with Daphne Koller
Tom interviews Daphne Koller, a Stanford professor turned serial entrepreneur. Daphne is widely known for her research at the intersection of machine learning and probabilistic reasoning.Daphne is a…
Self-Driving Cars in the 1980s (!) with Dean Pomerleau
Tom meets with Dr. Dean Pomerleau, who as a CMU PhD student in the 1980s was the first person to demonstrate that a neural network could be trained to automatically steer a self-driving…
Machine Learning Meets Statistics with Michael I. Jordan
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…
Machine Learning Theory with Leslie Valiant
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…
Decision Tree Learning with Ross Quinlan
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…
Reinforcement Learning with Rich Sutton
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…
The Chaotic Evolution of the Field with Tom Dietterich
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…
A University and Corporate Perspective with Yann LeCun
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…
Five Decades of Neural Networks with Geoffrey Hinton
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…
The History of Machine Learning with Tom Mitchell
Tom Mitchell, Founders University Professor at Carnegie Mellon University kicks off the podcast with this recording of his February 2026 seminar talk on “The History of Machine Learning.”He takes us…
Frequently Asked Questions
Machine Learning: How Did We Get Here? has published 14 episodes since February 2026, covering topics in History, Technology.
Machine Learning: How Did We Get Here? is currently moderate with new episodes weekly. Average episode length is 46m.
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