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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 | Carnegie Mellon University
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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
  • Self-Driving Cars in the 1980s (!) with Dean Pommerleau

    Self-Driving Cars in the 1980s (!) with Dean Pommerleau
    Apr 13 2026

    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 vehicle.

    Dean's results shocked the research community, and paved the way for decades of follow-on research leading to today's self-driving cars.
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    33 mins
  • Machine Learning meets Statistics with Michael Jordan

    Machine Learning meets Statistics with Michael Jordan
    Apr 6 2026

    Tom sits down with Michael 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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    1 hr and 1 min
  • Machine Learning Theory with Leslie Valiant

    Machine Learning Theory with Leslie Valiant
    Mar 30 2026

    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.
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    21 mins
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