Foundations of Machine Learning Boot Camp

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source: Simons Institute     2017年1月23日
Foundations of Machine Learning Boot Camp
Organizers: Sanjoy Dasgupta (UC San Diego), Sanjeev Arora (Princeton University), Nina Balcan (Carnegie Mellon University), Peter Bartlett (UC Berkeley), 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:
Elad Hazan (Princeton University): Optimization of Machine Learning
Andreas Krause (ETH Zürich) and Stefanie Jegelka (MIT): Submodularity: Theory and Applications
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
Tamara Broderick (MIT) and Michael Jordan (UC Berkeley): Nonparametric Bayesian Methods: Models, Algorithms, and Applications
Ruslan Salakhutdinov (Carnegie Mellon University): Tutorial on Deep Learning
Daniel Hsu (Columbia University): Tensor Decompositions for Learning Latent Variable Models
Percy Liang (Stanford University): Natural Language Understanding: Foundations and State-of-the-Art
For more information, please visit https://simons.berkeley.edu/workshops/machinelearning2017...
These presentations were supported in part by an award from the Simons Foundation.

Optimization for Machine Learning I Elad Hazan, Princeton University
https://simons.berkeley.edu/talks/ela... 1:05:21
Optimization for Machine Learning II 1:03:37
Submodularity: Theory and Applications I 1:04:01
Submodularity: Theory and Applications II 1:03:35
A Tutorial on Reinforcement Learning I 1:01:29
A Tutorial on Reinforcement Learning II 55:49
Interactive Learning of Classifiers and Other Structures 1:30:33
Deep Robotic Learning 1:35:22
Nonparametric Bayesian Methods: Models, Algorithms, and Applications I 1:06:01
Nonparametric Bayesian Methods: Models, Algorithms, and Applications II 1:03:45
Nonparametric Bayesian Methods: Models, Algorithms, and Applications III 1:02:05
Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV 1:03:33
Tutorial on Deep Learning I 1:01:53
Tutorial on Deep Learning II 55:52
Tutorial on Deep Learning III 56:42
Tutorial on Deep Learning IV 1:00:19
Tensor Decompositions for Learning Latent Variable Models I 55:49
Tensor Decompositions for Learning Latent Variable Models II 53:12
Natural Language Understanding: Foundations and State-of-the-Art 1:31:01