Event Description:
Machine learning (ML) in the form of standard supervised classification algorithms is not all that useful or productive in the natural sciences, because the (effective) goals of these algorithms aren't very similar to the goals of scientific inquiry. However, the ML community has delivered great ideas and methods for building, fitting, and validating extremely flexible models. David W. Hogg argues that if we want to exploit the good things about ML but achieve truly scientific goals, we need to do two things: We need to augment or modify the (currently trivial) causal structure of the ML methods to represent our very strong domain-specific beliefs about how the data are generated. And we need to be careful to use ML methods only in the parts of our problems for which we don't care about the latent structure or parameters (that is, use them to model nuisances, not use them to do everything). He gives examples from stellar astrophysics where adding ML components into larger causal models has created new scientific capabilities.
Event Speaker:
David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University.
Event Information:
This event is free and open to the public however registration is required via Eventbrite. A live-stream is also available on the event day. Hosted by the NYC Data Science Seminar Series.
