Saturday Morning Videos: Foundations of Machine Learning Boot Camp, Simons Institute, Berkeley

This past week saw a series of tutorial presentations in a "bootcamp" on the Foundations of Machine Learning at the Simons Institute at Berkeley organized by Sanjoy Dasgupta, Sanjeev Arora , Nina Balcan, Peter Bartlett, Sham Kakade, Santosh Vempala. Links to the videos can be found directly from the page:

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
• Lecture 1: Optimization for Machine Learning I
• Lecture 2: Optimization for Machine Learning II 

• Andreas Krause (ETH Zürich) and Stefanie Jegelka (MIT): Submodularity: Theory and Applications
• Lecture 1: Submodularity: Theory and Applications I
• Lecture 2: Submodularity: Theory and Applications II

  

Tuesday, January 24th

• Emma Brunskill (Carnegie Mellon University): A Tutorial on Reinforcement Learning
• Lecture 1: A Tutorial on Reinforcement Learning I
• Lecture 2: A Tutorial on Reinforcement Learning II 

• Sanjoy Dasgupta (UC San Diego) and Rob Nowak (University of Wisconsin-Madison): Interactive Learning of Classifiers and Other Structures
• Lecture 1: Interactive Learning of Classifiers and Other Structures 

• Sergey Levine (UC Berkeley): Deep Robotic Learning
• Lecture 1: Deep Robotic Learning 

Wednesday, January 25th

• Tamara Broderick (MIT) and Michael Jordan (UC Berkeley): Nonparametric Bayesian Methods: Models, Algorithms, and Applications
• Lecture 1: Nonparametric Bayesian Methods: Models, Algorithms, and Applications I
• Lecture 2: Nonparametric Bayesian Methods: Models, Algorithms, and Applications II
• Lecture 3: Nonparametric Bayesian Methods: Models, Algorithms, and Applications III
• Lecture 4: Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV 

Thursday, January 26th

• Ruslan Salakhutdinov (Carnegie Mellon University): Tutorial on Deep Learning
• Lecture 1: Tutorial on Deep Learning I
• Lecture 2: Tutorial on Deep Learning II 
• Lecture 3: Tutorial on Deep Learning III
• Lecture 4: Tutorial on Deep Learning IV  

Friday, January 27th

• Daniel Hsu (Columbia University): Tensor Decompositions for Learning Latent Variable Models
• Lecture 1: Tensor Decompositions for Learning Latent Variable Models I
• Lecture 2: Tensor Decompositions for Learning Latent Variable Models II  

• Percy Liang (Stanford University): Natural Language Understanding: Foundations and State-of-the-Art
• Lecture 1: Natural Language Understanding: Foundations and State-of-the-Art

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