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.

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.

Tom speaks with Ross Quinlan, whose algorithms C4.5 and ID3 helped establish decision trees as one of the most popular approaches in machine learning, and who founded RuleQuest Research, which accelerated the commercial adoption of machine learning.

Ross (published as "JR Quinlan") describes a sabbatical visit to Stanford University where he took a course that drove him to invent the first successful learning algorithm for decision trees, follow-on research that led to decision trees becoming one of the most popular machine learning algorithms, and his experience moving from academia into the commercial world.