Machine Learning and its Application for Finance

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Finance

Course type:

Elective course

When:

1 year, 3, 4 module

Course Syllabus

Abstract

During this practically oriented data science module students will learn how machine learning uses computers to run predictive models. The main objective is to explore existing data to build new knowledge, forecast future behavior, anticipate outcomes and trends. Explore theory and practice, and work with tools like Python to solve advanced data science problems.

Learning Objectives

• Make students able to collect, store, process and analyze data automatically with the use of scripting languages
• Make students able to develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
• Make students able to solve economic, financial and managerial problems using the best practices of data analysis using modern computational tools
• Make students able to identify the data needed for addressing the financial and business objectives

Expected Learning Outcomes

• Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of finance
• Choose methods adequately corresponding to the objectives of a research project
• Collect, store, process and analyze data automatically with the use of scripting languages; develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
• Planning and beginning to perform a research project requires an open and innovative mindset.
• Students should know how to: use ICT solutions in solving real-life problems, work together with other team members, develop personal knowledge and skills.

Course Contents

• Introduction to Python
• Managing Datasets in Python, Getting Data from Web
• Introduction in Statistics for Machine Learning
• Machine Learning Algorithms in Finance

Assessment Elements

• Homework in Python
• Lab in Python
• Activity on Seminars
• Project in Python

Interim Assessment

• 2024/2025 3rd module
  0.3 * Activity on Seminars + 0.15 * Homework in Python + 0.25 * Lab in Python + 0.3 * Project in Python

Bibliography

Recommended Core Bibliography

• An introduction to statistical learning with applications in R, , 2013
• An introduction to statistical learning with applications in R, , 2021
• Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
• Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.
• Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

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
• Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.

Authors

• SOLOVEVA EKATERINA EVGENEVNA