Michael I. Jordan: Machine Learning: Dynamical, Stochastic & Economic Perspectives
Channel: Purdue Engineering
Date: 2019-05-02

2019 Purdue Engineering Distinguished Lecture Series presenter Dr. Michael I. Jordan

While there has been significant progress at the interface of statistics and computer science in recent years, many fundamental challenges remain. Some are mathematical and algorithmic in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to cope with scarcity and provide incentives in learning-based two-way markets. Jordan's seminar present recent progress on each of these fronts.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. 

Full Abstract and Bio:  http://bit.ly/jordan-seminar

More on this lecture series: http://bit.ly/DistinguishedLectureSeries

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