2019 Machine Learning at the Flatiron Institute Seminar Series

Date	Speaker	Title
January 24, 2019	Noam Brown	Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
February 5, 2019	Aditya Mishra	Low-rank and sparse structure in modeling multivariate outcome
February 26, 2019	Peter Battaglia	Learning structured models of physics
March 14, 2019	Leslie Greengard	Fast multipole methods
April 2, 2019	Bin Yu	Three principles of data science: predictability, computability, and stability (PCS)
April 4, 2019	Miles Stoudenmire	Tensor Networks for Machine Learning
April 24, 2019	Yashar Hezaveh	Estimating the uncertainties of neural networks predictions
May 30, 2019	Charles Windolf	Neural Computations via Capsules
June 20, 2019	Giacomo Torlai	Reconstructing quantum states with Boltzmann machines
October 24, 2019	Miles Cranmer	Introduction to Hamiltonian Neural Networks
November 7, 2019	Dan Foreman-Mackey	Overview of Gaussian Processes for scientific computing
November 21, 2019	Erik Henning Thiede	Group theory for machine learning in the presence of symmetry