Master
2024/2025
Markov Chains
Type:
Compulsory course (Math of Machine Learning)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
1 year, 2, 3 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Math of Machine Learning
Language:
English
ECTS credits:
6
Course Syllabus
Abstract
The course is an introduction to the theory of Markov chains, an area of modern probability theory widely used in applications. In this course, we will start from the theory of finite state space Markov chains and continue with the general case of Markov chains with arbitrary state space. We will cover various ergodicity results for Markov kernels and relations between them, central limit theorem for Markov chains, and applications of Markov chains. In terms of applications, we will consider the Markov Chain Monte Carlo methods and study some most classical examples of algorithms of this family.