MACE1 DSBA 23-24

About

The specialization seminar offers the opportunity to study subjects and sections of mathematical statistics related to the application of differential equations, machine learning, probability theory and mathematical for modeling various solutions of a wide range of theoretical and applied problems. These tasks include modelling of real-world systems using catastrophe theory and self-organization theory. The computational methods used are standard for machine learning: clustering, pattern recognition, dimension reduction. The purpose of the research seminar is to expand the research horizons of students. It is assumed that at the end of the course, the student will be able to prepare a research paper or grant application. To do this, the student will be involved in the following activities: attending classes (it is obligatory), analyzing a large number of sources in a foreign area for the student in order to learn how to highlight mathematical problems in non-mathematical articles, completing part of a group project, preparing presentations and discussion (peer review) of other people's projects and presentations. Prerequisites Knowledge of basic mathematics: analysis, linear algebra, probability theory, - algorithms, programming fundamentals, the ability to understand computational packages.

Materials

Syllabus

Lecturer

Vasilii Gromov - vgromov@hse.ru

Assessments

In each module (1-3) there must be the one colloquium and one presentation per each group. During module 4 there must be the one term-project presentation per each student.

Grading formula

Final grade = 0,2 * Module 1 Presentation + 0,1 * Module 1 Colloquium + 0,2 * Module 2 Presentation + 0,1 * Module 2 Colloquium + 0,2 * Module 3 Presentation + 0,1 * Module 3 Colloquium + 0,1 * Module 4 TermProject