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Colloquium: Data-driven Models for the Physical Sciences

Colloquium: Data-driven Models for the Physical Sciences

There is immense hype, and immense promise, in machine learning for physics and astronomy. I use the case of stellar astrophysics as an example area in which to explore these ideas. It is an ideal field, because there are both very large data sets and incredibly detailed and successful physical models. And yet these models are nonetheless...

Location: Peyton Hall , Room 145

Speaker(s):

David W. Hogg

NYU

Data-driven Models for the Physical Sciences

Colloquium: Learning Certifiably Optimal Rule Lists for Categorical Data

Colloquium: Learning Certifiably Optimal Rule Lists for Categorical Data

Joint Seminar with Quantitative Social Science Colloquium, Program for Quantitative and Analytical Political Science, Department of Politics.

Location: 127 Corwin

Speaker(s):

Cynthia Rudin

Duke University Department of Computer Science

Learning Certifiably Optimal Rule Lists for Categorical Data

Colloquium: Machine Learning at Facebook

Machine intelligence for processing big data sets is big business. A statistical mathematician's point of view has led to (1) effective large-scale principal component analysis and singular value decomposition, and (2) some theoretical foundations for convolutional networks (convolutional networks underpin the recent revolution in artificial...

Location: 214 Fine Hall

Speaker(s):

Mark Tygert

Facebook Research

Machine Learning at Facebook

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Events Archive

Colloquium: Data-driven Models for the Physical Sciences

Colloquium: Learning Certifiably Optimal Rule Lists for Categorical Data

Colloquium: Machine Learning at Facebook

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