#68 Probabilistic Machine Learning & Generative Models, with Kevin Murphy
Learning Bayesian Statistics
Is Bason Machine Learning a Good Prediction Tool?
In machine learning, the focus is much more on predictive accuracy. If you just compute a point estimate, you could think of that as poor man's base. The frequentest approach is ekow an entirely ifferent philosophy I about repeated trials.
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Hosting someone like Kevin Murphy on your podcast is… complicated. Not because Kevin himself is complicated (he’s delightful, don’t make me say what I didn’t say!), but because all the questions I had for him amounted to a 12-hour show.
Sooooo, brace yourselves folks!
No, I'm kidding. Of course I didn’t do that folks, Kevin has a life! This life started in Ireland, where he was born. He grew up in England and got his BA from the University of Cambridge. After his PhD at UC Berkeley, he did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California in 2011 on his sabbatical and then ended up staying.
He currently runs a team of about 8 researchers inside of Google Brain working on generative models, optimization, and other, as Kevin puts it, “basic” research topics in AI/ML. He has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and the last one coming in 2023. You may be familiar with his 2012 book, as it was awarded the DeGroot Prize for best book in the field of statistical science.
Outside of work, Kevin enjoys traveling, outdoor sports (especially tennis, snowboarding and scuba diving), as well as reading, cooking, and spending time with his family.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Kevin’s website: https://www.cs.ubc.ca/~murphyk/
- Kevin on Twitter: https://mobile.twitter.com/sirbayes
- Kevin’s books (free pdf) on GitHub (includes a link to places where you can buy the hard copy): https://probml.github.io/pml-book/
- Book that inspired Kevin to get into AI: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
- State-space models library in JAX (WIP): https://github.com/probml/ssm-jax
- Other software for the book (also in JAX): https://github.com/probml/pyprobml
- Fun photo of Kevin’s hacked-up wearable camera system from 20 years ago: https://www.cs.ubc.ca/~murphyk/Vision/placeRecognition.html
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