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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)

Show List of Lecturers

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.

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. http://web.archive.org/web/20111203000912/http://www.amazon.com:80/ProbabilityPath-Sidney-Resnick/dp/081764055X).
  • Statistics
    (e.g. http://web.archive.org/web/20150207093201/https://www.amazon.com/Statistical-InferenceGeorge-Casella/dp/0534243126).
  • 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.

Keywords and Mathematics Subject Classification (MSC)
  • 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