The Decision-Making Side of Machine Learning
Michael I. Jordan
November 17, 2021 / 1:00-2:00 p.m. ET
EAI Distinguished Lecturer Series
Much of the recent focus in machine learning has been on the pattern-recognition side of the field. I will focus instead on the decision-making side, where many fundamental challenges remain. Some are statistical in nature, including the challenges associated with multiple decision-making, some are algorithmic, including the challenge of coordinated decision-making on distributed platforms, and others are economic, involving learning systems that must cope with scarcity and competition. I will present recent progress on each of these fronts, focusing on the economic front.
Biography
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of EECS and the Department of Statistics at the University of California, Berkeley. His research interests include machine learning, optimization, and control theory. Prof. Jordan is a member of the National Academy of Sciences and the National Academy of Engineering, and is a Foreign Member of the Royal Society (UK). He has given a Plenary Lecture at the International Congress of Mathematicians, he has received the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the AMS Ulf Grenander Prize in Stochastic Theory and Modeling, the David Rumelhart Prize, the ACM/AAAI Allen Newell Award, and he holds an Honorary Doctorate from Yale University.