SIAM Mathematics of Data Science (MDS20) Distinguished Lecture Series: Michael I. Jordan

Machine Learning: Dynamical, Statistical, and Economic Perspectives
Abstract: 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. Others are economic, involving learning systems that must cope with scarcity and competition. I'll pose, and perhaps even solve, a few algorithmic problems in these areas, making use of a line of recent work on (continuous-time) dynamical systems perspectives on optimization and diffusion.
Michael I. Jordan, University of California, Berkeley, U.S.
This is one of seven virtual plenary talks originally scheduled for the 2020 SIAM Conference on Mathematics of Data Science. For more information on this session, visit https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=69238. To view the virtual program and register for other invited plenary talks, minitutorial talks, and minisymposia, please visit the MDS20 website at https://www.siam.org/conferences/cm/conference/mds20.

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