Bachelor 2026/2027

Economic Applications of Machine Learning

Type: Elective course (Economics)

Delivered by: Department of Theoretical Economics

Where: Faculty of Economic Sciences

When: 4 year, 1 module

Open to: students of one campus

Instructors: Pasha Andreyanov

Language: English

ECTS credits: 3

Contact hours: 36

Course Syllabus

Abstract

The course describes main recent machine learning and data analysis methods as well as their application in economic research. Special attention in the course is paid to the implementation of these algorithms and models in Python. There will we 3 individual written assessments and 1 group oral assessment. Problem sets submitted before the deadline weight 1:1. Problem sets submitted after the deadline weight 2:1. If a student does not get a passing grade by the end of the course, there will be 2 makeups.

Learning Objectives

• Within this course students learn the key methods of data analysis, examples of their application to economic research and learn how to build on their own considered models in Python.

Expected Learning Outcomes

• Code a Logit regression from scratch, run a classic Logit regression in Python, know alternative Logit regressions.
• Know and distinguish various non-parametric methods, such as KDE and RD.
• Know how to run a gradient boosting.
• Know how to run a simplest neural network.
• Know how to run and visualize a regression. Write an OLS regression from scratch.
• Know various SVM methods.
• Know where to find data, understand the formats, know how to work with it and do simple operations on it.
• To understand decision trees, forests; run them in Python and present the results.
• Understand how Logit fits into a broader family of classification methods.

Course Contents

• 1. Data
• 2. Regressional and visual analysis
• 3. Logit
• 5. Classification
• 6. Support vector machine
• 4. Nonparametric analysis
• 7. Decision trees
• 8. Decision Trees
• 9. Neural networks

Assessment Elements

• Group Project Presentation
  A group of 3 - 5 students defends (presents) a group project of their choosing. Typically a dataset from kaggle is chosen. It can not be a homework or anything from another class.
• Attendance
  Attendence
• Quizzes
  Several (2 or 3) written quizzes at the end of the lectures covering the material that was explained in the lecture
• Homework
  A mandatory (blocking) homework containing simple exercises in regressional analysis and dynamic programming.

Interim Assessment

• 2026/2027 1st module
  0.2 * Quizzes + 0.4 * Group Project Presentation + 0.1 * Attendance + 0.3 * Homework

Bibliography

Recommended Core Bibliography

• Дмитриев Егор Андреевич. (2017). Линейная регрессия. Students’ Scientific Research and Development ; № 2(4) ; 123-124 ; Научные Исследования и Разработки Студентов.
• Красногир, Е. Г. (2009). Непараметрические ядерные оценки Надарая–Ватсона и область их задания.

Recommended Additional Bibliography

• Aguirregabiria, V., & Carro, J. M. (2021). Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models.
• Wiktor Budziński, & Mikołaj Czajkowski. (2021). Accounting for Spatial Heterogeneity of Preferences in Discrete Choice Models. Central European Journal of Economic Modelling and Econometrics (CEJEME), 13(1), 1–24. https://doi.org/10.24425/cejeme.2021.136456

Authors

• MAMEDLI MARIAM OKTAEVNA
• Andreianov Pavel Pavlovich