2024/2025
Machine Learning and its Application for Finance
Type:
Mago-Lego
Delivered by:
Department of Finance
When:
3 module
Open to:
students of one campus
Instructors:
Evgenii Chermoshentsev
Language:
English
ECTS credits:
3
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
Interim Assessment
- 2024/2025 3rd module0.3 * 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.