Research Seminar "Data Analysis in the Natural Sciences"

2022/2023

Academic Year

ENG

Instruction in English

8

ECTS credits

Delivered at:

Big Data and Information Retrieval School

Course type:

Elective course

When:

4 year, 1-3 module

Instructors

Hushchyn, Mikhail

Derkach, Denis

Konovalov, Dmitrii

Poptsova, Maria

Ramazyan, Tigran

Ratnikov, Fedor

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.
• Forward problem solution using generative modeling.
• Uncertainty estimation for machine-learning based solution.
• Databases of bacterial genomes
• UCSC genome browser. The human genome.
• GWAS Catalog, eupedia, phenotype-Genotype Integrator, plink
• BLAST

Assessment Elements

• Physics project
• Bioinformatics project

Interim Assessment

• 2022/2023 3rd module
  0.5 * Bioinformatics project + 0.5 * Physics project

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

• 9781491912140 - Vanderplas, Jacob T. - Python Data Science Handbook : Essential Tools for Working with Data - 2016 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1425081 - nlebk - 1425081
• 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

Authors

• Галевская Софья Андреевна