Distinguished Speaker Series: Machine Learning: Trends, Perspectives and Challenges
Prof. Michael I. Jordan, Department of EECS, Department of Statistics, AMP Lab, Berkeley AI Research Lab, UC Berkeley
While there has been significant progress in the theory and practice in machine learning in recent years, many fundamental challenges remain. Some are mathematical in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical in nature, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to provide services and incentives in data-based markets. And others are systems challenges, arising from the need for highly-scalable, robust and understandable hardware and software platforms. I will overview these challenges and others, and propose some paths forward.
Bio: 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. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009.