Article Source

• The Four Pillars of Machine Learning

The Four Pillars of Machine Learning

Abstract

I will present a unified perspective on the field of machine learning, following the structure of my recent book, “Probabilistic Machine Learning: Advanced Topics” which is centered on the “4 pillars of ML”: predictions, decisions, discovery and generation. For each of these tasks, I will give a brief summary of some recent methods, including a few of my own contributions. Probabilistic Machine Learning: Advanced Topics

• https://probml.github.io/pml-book/book2.html

Bio

Kevin was born in Ireland, but grew up in England. He got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He then 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 on his sabbatical and then ended up staying. He currently runs a team of 6 researchers inside of Google Brain; the team works on generative models, Bayesian inference, and various other topics. Kevin has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and 2023 by MIT Press. (The 2012 book was awarded the DeGroot Prize for best book in the field of Statistical Science.) Kevin was also the (co) Editor-in-Chief of JMLR 2014–2017.