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Machine Learning: How Did We Get Here?

Machine Learning: How Did We Get Here?

By: Tom Mitchell | Stanford Digital Economy Lab
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About this listen

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.© 2026 Stanford Digital Economy Lab. All rights reserved. World
Episodes
  • Decision Tree Learning with Ross Quinlan
    Mar 23 2026

    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.

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    24 mins
  • Reinforcement Learning with Rich Sutton
    Mar 16 2026

    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.

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    34 mins
  • The Chaotic Evolution of the Field with Tom Dietterich
    Mar 9 2026

    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.

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    1 hr and 5 mins
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