Dr. Marwa Banna

High-dimensional Probability with Applications to Big Data Sciences

(Summer Semester 2019/20)

Lecture Announcement

The lecture will be given in English.



Lectures Notes

Course description

With the fast growth of data sciences, there was a dramatic surge of interest and activity over the past two decades in high-dimensional probability that provides vital methods and tools for a wide range of applications. High-dimensional probability is the area of probability theory that studies random objects in R^n, where the dimension n can be very large. As classical probabilistic tools are no longer sufficient for most of the modern applications in data sciences, these lectures intend to cover partially this gap. The focus of the lectures is the non-asymptotic theory in high-dimensional probability with a view towards modern applications in big data sciences. Here is an incomplete list of topics that will be covered: Applications:


Date : Tuesday 14:00 -- 16:00
Place: online via the platform Zoom.



The lectures are self-contained and are open for students who have a good knowledge in linear algebra, measure theory and have succeeded Stochastics I. We will primarily rely on the following textbooks:

Other books on concentration inequalities:

  • Stéphane Boucheron, Gábor Lugosi, and Pascal Massart, Concentration Inequalities: A Nonasymptotic Theory of Independence, Oxford University Press 2013.
  • Michel Ledoux, Concentration of measure phenomenon, American Mathematical Society 2001.