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Summer Graduate School

Mathematics of Machine Learning July 29, 2019 - August 09, 2019
Parent Program: --
Location: University of Washington, Seattle
Organizers Sebastien Bubeck (Microsoft Research), Anna Karlin (University of Washington), Adith Swaminathan (Microsoft Research)
Speaker(s)

Show List of Speakers

Description
Image
Popular visualization of the MNIST dataset

Learning theory is a rich field at the intersection of statistics, probability, computer science, and optimization. Over the last decades the statistical learning approach has been successfully applied to many problems of great interest, such as bioinformatics, computer vision, speech processing, robotics, and information retrieval. These impressive successes relied crucially on the mathematical foundation of statistical learning.

Recently, deep neural networks have demonstrated stunning empirical results across many applications like vision, natural language processing, and reinforcement learning. The field is now booming with new mathematical problems, and in particular, the challenge of providing theoretical foundations for deep learning techniques is still largely open. On the other hand, learning theory already has a rich history, with many beautiful connections to various areas of mathematics (e.g., probability theory, high dimensional geometry, game theory). The purpose of the summer school is to introduce graduate students (and advanced undergraduates) to these foundational results, as well as to expose them to the new and exciting modern challenges that arise in deep learning and reinforcement learning.

To get the most out of the mini-courses in the school, students are encouraged to attend all the lectures and minimize distractions. Please try to avoid the use of laptops, smartphones, tablets, etc. except for note-taking (because the material is highly mathematical, students will probably find it easier to use a pen and notebook).

For eligibility and how to apply, see the Summer Graduate Schools homepage

Suggested Prerequisites:

  • Linear Algebra
    (e.g. https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/).
  • Probability
    (e.g. https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/).
  • Multivariable calculus
    (e.g. https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/).
  • Real Analysis
    (e.g. https://ocw.mit.edu/courses/mathematics/18-100c-real-analysis-fall-2012/).

Due to the small number of students supported by MSRI, only one student per institution will be funded by MSRI.

This summer school is brought to you through the funding support of the academic sponsoring institutions of the Mathematical Sciences Research Institute, Berkeley, CA,  Microsoft Research, and the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and in cooperation with the Algorithmic Foundations of Data Science Institute at the University of Washington.

Keywords and Mathematics Subject Classification (MSC)
Tags/Keywords
  • Learning and adaptive systems

  • Computational learning theory

  • Decision theory

  • Probabilistic games

  • stochastic processes

  • Neural nets

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification No Secondary AMS MSC
Schedule, Notes/Handouts & Videos
Show Schedule, Notes/Handouts & Videos
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Jul 29, 2019
Monday
08:00 AM - 08:30 AM
  Coffee and Badge pick-up (Zillow Commons)
08:30 AM - 08:45 AM
  Welcome
09:00 AM - 10:00 AM
  Statistical Learning
Robert Schapire (Microsoft Research)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Convex Optimization
Sebastien Bubeck (Microsoft Research)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Statistical Learning
Robert Schapire (Microsoft Research)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Jul 30, 2019
Tuesday
09:00 AM - 10:00 AM
  Statistical Learning
Robert Schapire (Microsoft Research)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Convex Optimization
Sebastien Bubeck (Microsoft Research)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Statistical Learning
Robert Schapire (Microsoft Research)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Jul 31, 2019
Wednesday
09:00 AM - 10:00 AM
  Bandits
Kevin Jamieson (University of Washington)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Statistical Learning
Robert Schapire (Microsoft Research)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Bandits
Kevin Jamieson (University of Washington)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Aug 01, 2019
Thursday
09:00 AM - 10:00 AM
  Bandits
Kevin Jamieson (University of Washington)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Convex Optimization
Sebastien Bubeck (Microsoft Research)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Bandits
Kevin Jamieson (University of Washington)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Aug 02, 2019
Friday
09:00 AM - 10:00 AM
  Bandits
Kevin Jamieson (University of Washington)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Convex Optimization
Sebastien Bubeck (Microsoft Research)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Convex Optimization
Sebastien Bubeck (Microsoft Research)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Aug 05, 2019
Monday
09:00 AM - 10:00 AM
  Deep Learning
Joan Bruna (New York University, Courant Institute)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Reinforcement Learning
Emma Brunskill (Stanford University)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Reinforcement Learning
Emma Brunskill (Stanford University)
Aug 06, 2019
Tuesday
09:00 AM - 10:00 AM
  Reinforcement Learning
Emma Brunskill (Stanford University)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Deep Learning
Joan Bruna (New York University, Courant Institute)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Reinforcement Learning
Emma Brunskill (Stanford University)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Aug 07, 2019
Wednesday
09:00 AM - 10:00 AM
  Reinforcement Learning
Emma Brunskill (Stanford University)
10:00 AM - 10:30 AM
  Coffee Break
10:30 AM - 11:30 AM
  Deep Learning
Joan Bruna (New York University, Courant Institute)
11:30 AM - 12:30 PM
  Problem Session
12:30 PM - 02:00 PM
  Lunch
02:00 PM - 03:00 PM
  Deep Learning
Joan Bruna (New York University, Courant Institute)
03:00 PM - 03:30 PM
  Coffee Break
03:30 PM - 04:30 PM
  Problem Session
Aug 08, 2019
Thursday
All Day
  Field Trip to Microsoft Research Redmond
Aug 09, 2019
Friday
09:00 AM - 12:30 PM
  Student presentations and wrap-up
12:30 PM - 02:00 PM
  Lunch