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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
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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):

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