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Machine Learning and its Application for Finance

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 3 модуль

Преподаватель


Чермошенцев Евгений Андреевич

Course Syllabus

Abstract

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

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 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

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

Course Contents

  • Data Analysis in MS Excel
  • Introduction to Python
  • Managing Datasets in Python
  • Data Visualisation
  • Getting Data from Web
  • Machine Learning Algorithms in Finance
Assessment Elements

Assessment Elements

  • non-blocking Lab in MS Excel
  • non-blocking Hometask in Python
  • non-blocking Lab in Python
  • non-blocking Project in Python
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.25 * Lab in Python + 0.25 * Lab in MS Excel + 0.25 * Hometask in Python + 0.25 * Project in Python
Bibliography

Bibliography

Recommended Core Bibliography

  • Danielle Stein Fairhurst (2015). Using Excel for Business Analysis
  • 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

  • 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.