Кто читает:: Департамент больших данных и информационного поиска

Статус:: Курс по выбору

Когда читается:: 4-й курс, 1-3 модуль

Преподаватели

Громов Василий Александрович

Деркач Денис Александрович

Рамазян Тигран Арменович

Ратников Федор Дмитриевич

Full Syllabus

Abstract

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 analysis and forecasting of time series, automatic detection of trend changes, forecasting “black swan” events, and analysis of stable configurations in the community. 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

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.
Forward problem solution using generative modeling.
Uncertainty estimation for machine-learning based solution.
Diploma topic defence
Scientific papers presentation
Selected scientific topics context analysis
Kolloquium

Assessment Elements

Module 1 assignment 0.3 = Module 1 presentation * 0.2 + Module 1 colloquium * 0.1
During module 1 there must be at least one presentation per each group and an obligatory colloquium will be conducted at the end of the module.
Mod. 2 assignment 0.4 = Mod. 2 presentation*0.2 + Mod. 2 colloquium*0.1+BSc Thesis topic defence*0.1
During module 2 there must be at least one presentation per each group, an obligatory colloquium will be conducted at the end of the module, and a defence of the chosen BSc thesis topic.
Module 3 assignment 0.3 = Module 3 presentation * 0.2 + Module 3 colloquium * 0.1
During module 3 there must be at least one presentation per each group and an obligatory colloquium will be conducted at the end of the module.

Interim Assessment

2023/2024 3rd module
Module 1 assignment * 0.3 + Module 2 assignment * 0.4 + Module 3 assignment * 0.3

Bibliography

Recommended Core Bibliography

Ernest P. Chan. (2021). Quantitative Trading : How to Build Your Own Algorithmic Trading Business. Wiley.
Gabaix, X., Gopikrishnan, P., Plerou, V., & Stanley, H. E. (2003). A theory of power-law distributions in financial market fluctuations. Nature, 423(6937), 267. https://doi.org/10.1038/nature01624
Irene Aldridge. (2013). High-Frequency Trading : A Practical Guide to Algorithmic Strategies and Trading Systems: Vol. 2nd edition. Wiley.
Joel Hasbrouck. (2007). Empirical Market Microstructure : The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press.
Mike Elvin. (2004). Financial Risk Taking : An Introduction to the Psychology of Trading and Behavioural Finance. Wiley.

Recommended Additional Bibliography

Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081

Бакалаврская программа «Прикладной анализ данных»

Контакты

Research Seminar "Data Analysis in the Natural Sciences"