Spring 2017

Foundations of Machine Learning Boot Camp

Jan. 23Jan. 27, 2017

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Sanjeev Arora (Princeton University), Nina Balcan (Carnegie Mellon University), Peter Bartlett (UC Berkeley), Sanjoy Dasgupta (UC San Diego), Sham Kakade (University of Washington), Santosh Vempala (Georgia Institute of Technology)

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

Visit the schedule page for the archived videos.