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Events Archive

Machine Learning and the Physical World

Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling).

Location: Julius Romo Rabinowitz 399

Guest Lecture: Algorithms of Oppression: How Search Engines Reinforce Racism

In Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities.

Location: 010 Pine

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