Research Seminar "Data Science in Applied Research 2"

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент анализа данных и искусственного интеллекта

Статус:

Курс обязательный

Когда читается:

4-й курс, 1-3 модуль

Преподаватели

Рамазян Тигран Арменович

Ратников Федор Дмитриевич

Сучкова Светлана Анатольевна

Course Syllabus

Abstract

The research seminar offers the opportunity to study methods and methodology of mathematical modelling and machine learning in the context of natural science problems. These tasks include fast simulation of high energy physics events, change point and anomaly detection in complex systems, and optimisation of experimental setup. The purpose of the seminar is to expand the research horizons and skills of students. At the end of the course, the students are expected to be able to present their findings and engage in peer review discussions freely.

Learning Objectives

• Be able to prepare and conduct a presentation with a report on a scientific topic, as well as conduct an academic discussion on the materials of the report.
• To be able to independently choose and study modern scientific articles, find relevant literature.
• Be able to write scientific texts.

Expected Learning Outcomes

• Be able to prepare and conduct a presentation with a report on a scientific topic, as well as conduct an academic discussion on the materials of the report.
• Methods for verifying empirical results: hypothesis testing, bootstrap, randomization, etc.
• Methods of mathematical modeling based on (stochastic) differential equations, probability theory.
• Modern computational methods used in related fields, in particular, when forecasting time series and solving inverse problems (Fourier analysis, wavelets, regression, SSA, dimension reduction, moving averages, neural networks, filters, etc. - understanding the advantages and disadvantages each of the methods.
• To be able to independently choose and study modern scientific articles, find relevant literature. Be able to write scientific texts.

Course Contents

• Scientific modeling and machine learning description.
• Diploma topic defence
• Scientific papers presentation
• Kolloquium

Assessment Elements

• Review 3
• Thesis topic defence
• Report
• Colloquim
• Reviews 1, 2
• Review 4

Interim Assessment

• 2026/2027 3rd module
  0.3 * Colloquim + 0.06 * Review 3 + 0.09 * Review 4 + 0.39 * Report + 0.1 * Thesis topic defence + 0.06 * Reviews 1, 2

Bibliography

Recommended Core Bibliography

• Algorithmic trading : winning strategies and their rationale, Chan, E. P., 2013
• Empirical market microstructure : the institutions, economics, and econometrics of securities trading, Hasbrouck, J., 2007

Recommended Additional Bibliography

• Pattern recognition and machine learning, Bishop, C. M., 2006

Authors

• RAMAZIAN TIGRAN ARMENOVICH
• Ratnikov Fedor Dmitrievich