Machine Learning: How Did We Get Here?
Tom Mitchell literally wrote the book on machine learning.
Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University · Stanford Digital Economy Lab
Show overview
Machine Learning: How Did We Get Here? has published 13 episodes during 2026. That works out to roughly 10 hours of audio in total. Releases follow a weekly cadence.
Episodes typically run thirty-five to sixty minutes — most land between 33 min and 1h 3m — though episode length varies meaningfully from one episode to the next. None of the episodes are flagged explicit by the publisher. It is catalogued as a EN-language Technology show.
The show is actively publishing — the most recent episode landed 1 weeks ago, with 13 episodes already out so far this year. Published by Stanford Digital Economy Lab.
From the publisher
Tom Mitchell literally wrote the book on machine learning. In this series of candid conversations with his fellow pioneers, Tom traces the history of the field through the people who built it. Behind the tech are stories of passion, curiosity, and humanity. Tom Mitchell is the University Founders Professor at Carnegie Mellon University, a Digital Fellow at the Stanford Digital Economy Lab, and the author of Machine Learning, a foundational textbook on the subject. This podcast is produced by the Stanford Digital Economy Lab.
Latest Episodes
AI Agents to Model Human Cognition with John Laird
Machine Learning and Speech Recognition with Kai-Fu Lee
Machine Learning meets Cognitive Neuroscience with Jay McClelland
Learning Probabilistic Models with Daphne Koller
Self-Driving Cars in the 1980s (!) with Dean Pomerleau
Machine Learning Meets Statistics with Michael I. Jordan
Ep 7Machine 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 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.
Ep 6Decision 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 accelerated the commercial adoption of machine learning.Ross (published as "JR Quinlan") describes a sabbatical visit to Stanford University where he took a course that drove him to invent the first successful learning algorithm for decision trees, follow-on research that led to decision trees becoming one of the most popular machine learning algorithms, and his experience moving from academia into the commercial world.
Ep 5Reinforcement 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 research on reinforcement learning.Rich discusses why the common framing of machine learning as 'supervised learning' is insufficient, and how reinforcement learning reframes the problem. He discusses how reinforcement learning has developed as a subfield of machine learning, the influence of Harry Kopf on his early thinking, his long-time collaboration with Andy Barto, his views about today's state of the art, and more.
Ep 4The 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 the field, and has served in professional roles from Executive Editor of the journal Machine Learning, to President of the Association for the Advancement of Artificial Intelligence. He shares his encyclopedic knowledge of the field and its evolution, describing waves of alternative paradigms, the interaction of theory with practice, the interaction of statisticians with computer scientists, some of his main research results, and his experience spinning off a machine learning startup company.
Ep 3A 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 for his research in neural network learning. Yann takes us from his days as a postdoc working with Geoffrey Hinton, through his days as Chief AI Scientist at Facebook/Meta. His simultaneous roles as a Professor at NYU and Chief AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art.
Ep 2Five 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 got into the field, from his days as an aspiring carpenter to his conversion to a neural network researcher. He explains the burst of neural network progress in the mid-1980s when the backpropagation training algorithm came into widespread use, and the re-emergence of deep neural networks in 2012 when he and his students soundly defeated the best computer vision methods around.Geoffrey discusses his early realization that those GPUs being sold to accelerate video games were the perfect hardware to accelerate neural networks as well, his journey from academia to Google, the competition among the big AI companies, and his views on where AI is and might be headed.
Ep 1The 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 from the writings of early philosophers about whether it is even possible to form correct general laws given only specific examples, to today’s machine learning algorithms that underlie a trillion dollar AI economy. Along the way we see the thoughts and recollections of many of the pioneers in the field, in the form of excerpts from upcoming podcast episodes featuring full interviews with each.Tom discusses the wonderful creativity and diversity of approaches explored during the 1980s, the integration of statistics and probability into the field in the 1990s and early 2000s, and the amazing progress over the past decade that has brought us today’s AI systems. He reflects in the end on what we should learn from this history.Recorded at Carnegie Mellon University.