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Course Syllabus

Bachelor 2025/2026

Building Scoring Models Using Machine Learning Methods

Type: Elective course (Software Engineering)
Delivered by: School of Software Engineering
When: 3 year, 3, 4 module
Open to: students of all HSE University campuses
Language: English
Contact hours: 60

Course Syllabus

Full SyllabusAsk Question

Abstract

This course describes how dramatically changes of the information market provides new power tools for financial data processing and analysis. In addition, we will compare classical and contemporary tools performance. The main aim of this course is a species of different country data sets and technics for integration this data for common international data environment.
Learning Objectives

Learning Objectives

  • The main aims of the course are providing knowledge about all types of public and private scoring systems, data sets for their creation and appropriate ML methods suitable for scoring development.
Expected Learning Outcomes

Expected Learning Outcomes

  • Providing knowledge about all types of public and private scoring systems, data sets for their creation and appropriate ML methods suitable for scoring development.
Course Contents

Course Contents

  • Contemporary financial analyses main challenges.
  • Big, data mining, data science.
  • Main data types.
  • Correlation.
  • Data processing. Modeling..
  • Estimation and model testing.
  • Practice.
  • Interpretable models and suggestion systems.
  • Applications. Scorings, rankings, ratings.
  • Modern challenges.
Assessment Elements

Assessment Elements

  • non-blocking Case discussions
  • non-blocking Home assignments
  • non-blocking Final exam
Interim Assessment

Interim Assessment

  • 2025/2026 4th module
    0.2 * Case discussions + 0.5 * Final exam + 0.3 * Home assignments
Bibliography

Bibliography

Recommended Core Bibliography

  • Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning, 2016. URL: http://www.deeplearningbook.org

Recommended Additional Bibliography

  • Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536

Authors

  • Butskaia Evgeniia Aleksandrovna
  • MUNERMAN Ilia VIKTOROVICH