
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... more
| Publishes | Twice weekly | Episodes | 3 | Founded | 9 days ago |
|---|---|---|---|---|---|
| Categories | TechnologyHistory | ||||

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... more
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 c... more
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 whe... more
People also subscribe to these shows.





How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.
Apple Podcasts | #205 | |
Apple Podcasts | #40 | |
Apple Podcasts | #70 | |
Apple Podcasts | #130 | |
Apple Podcasts | #240 |
Listeners, social reach, demographics and more for this podcast.
| Gender Skew | Location | Interests | |||
|---|---|---|---|---|---|
| Professions | Age Range | Household Income | |||
| Social Media Reach | |||||
Rephonic provides a wide range of podcast stats for Machine Learning: How Did We Get Here?. We scanned the web and collated all of the information that we could find in our comprehensive podcast database. See how many people listen to Machine Learning: How Did We Get Here? and access YouTube viewership numbers, download stats, audience demographics, chart rankings, ratings, reviews and more.
Rephonic provides a full set of podcast information for three million podcasts, including the number of listeners. View further listenership figures for Machine Learning: How Did We Get Here?, including podcast download numbers and subscriber numbers, so you can make better decisions about which podcasts to sponsor or be a guest on. You will need to upgrade your account to access this premium data.
Rephonic provides comprehensive predictive audience data for Machine Learning: How Did We Get Here?, including gender skew, age, country, political leaning, income, professions, education level, and interests. You can access these listener demographics by upgrading your account.
To see how many followers or subscribers Machine Learning: How Did We Get Here? has on Spotify and other platforms such as Castbox and Podcast Addict, simply upgrade your account. You'll also find viewership figures for their YouTube channel if they have one.
These podcasts share a similar audience with Machine Learning: How Did We Get Here?:
1. Hard Fork
2. The Ezra Klein Show
3. The Daily
4. Pivot
5. Many Minds
Machine Learning: How Did We Get Here? launched 9 days ago and published 3 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.
Our systems regularly scour the web to find email addresses and social media links for this podcast. We scanned the web and collated all of the contact information that we could find in our podcast database. But in the unlikely event that you can't find what you're looking for, our concierge service lets you request our research team to source better contacts for you.
Rephonic pulls ratings and reviews for Machine Learning: How Did We Get Here? from multiple sources, including Spotify, Apple Podcasts, Castbox, and Podcast Addict.
View all the reviews in one place instead of visiting each platform individually and use this information to decide if a show is worth pitching or not.
Rephonic provides full transcripts for episodes of Machine Learning: How Did We Get Here?. Search within each transcript for your keywords, whether they be topics, brands or people, and figure out if it's worth pitching as a guest or sponsor. You can even set-up alerts to get notified when your keywords are mentioned.