Couverture de Machine Learning: How Did We Get Here?

Machine Learning: How Did We Get Here?

Machine Learning: How Did We Get Here?

De : Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University
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 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.
Épisodes
  • Learning Probabilistic Models with Daphne Koller

    Learning Probabilistic Models with Daphne Koller
    Apr 20 2026

    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 member of the U.S. National Academy of Engineering, and is currently CEO of Insitro, a company at the intersection of machine learning and human biology.
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    40 min
  • Self-Driving Cars in the 1980s (!) with Dean Pomerleau

    Self-Driving Cars in the 1980s (!) with Dean Pomerleau
    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 min
  • Machine Learning Meets Statistics with Michael I. Jordan

    Machine Learning Meets Statistics with Michael I. Jordan
    Apr 6 2026

    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 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 h et 1 min
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