Master
2022/2023
Random Matrix Theory
Type:
Elective course (Math of Machine Learning)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Department of Complex System Modelling Technologies
Where:
Faculty of Computer Science
When:
2 year, 1 module
Mode of studies:
distance learning
Open to:
students of one campus
Master’s programme:
Math of Machine Learning
Language:
English
ECTS credits:
6
Contact hours:
64
Course Syllabus
Abstract
The aim of this course is to provide an introduction to asymptotic and non-asymptotic methods for the study of random structures in high dimension that arise in probability, statistics, computer science, and mathematics. One of the emphases is on the development of a common set of tools that has proved to be useful in a wide range of applications in different areas. Topics will include concentration of measure, Stein’s methods, suprema of random processes and etc. Another main emphasis is on the application of these tools for the study of spectral statistics of random matrices, which are remarkable examples of random structures in high dimension and may be used as models for data, physical phenomena or within randomised computer algorithms. The topics of this course form an essential basis for work in the area of high dimensional data.