Simons Institute Workshop: Foundations of Machine Learning Boot Camp

Seminar | January 23 | 9:30 a.m.-4:30 p.m. | Calvin Laboratory (Simons Institute for the Theory of Computing), Main Lecture Hall

 Various, Various

 Department of Mathematics

The Boot Camp is intended to acquaint program participants with the key themes of the program. It will consist of five days of tutorial presentations, each with ample time for questions and discussion, as follows:

Monday, January 23rd Elad Hazan (Princeton University): Optimization of Machine Learning Andreas Krause (ETH Zürich) and Stefanie Jegelka (MIT): Submodularity: Theory and Applications

Tuesday, January 24th Emma Brunskill (Carnegie Mellon University): A Tutorial on Reinforcement Learning Sanjoy Dasgupta (UC San Diego) and Rob Nowak (University of Wisconsin-Madison): Interactive Learning of Classifiers and Other Structures Sergey Levine (UC Berkeley): Deep Robotic Learning

Wednesday, January 25th Tamara Broderick (MIT) and Michael Jordan (UC Berkeley): Nonparametric Bayesian Methods: Models, Algorithms, and Applications

Thursday, January 26th Ruslan Salakhutdinov (Carnegie Mellon University): Tutorial on Deep Learning

Friday, January 27th Daniel Hsu (Columbia University): Tensor Decompositions for Learning Latent Variable Models Percy Liang (Stanford University): Natural Language Understanding: Foundations and State-of-the-Art

 nikhil@math.berkeley.edu