Бакалавриат 2024/2025

Экономические приложения машинного обучения

Статус: Курс по выбору (Экономика)

Направление: 38.03.01. Экономика

Кто читает: Департамент теоретической экономики

Где читается: Факультет экономических наук

Когда читается: 4-й курс, 1 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Андреянов Павел Павлович

Язык: английский

Кредиты: 3

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. All lectures will be held online in zoom, and seminars will be mixed online-offline. 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

• HW1
  The student will master the material explained in the first 2 lectures.
• HW2 (group)
  This group homework covers the material that did not enter the first homework.
• Group presentation
  This is a group presentation of a project involving machine learning.

Interim Assessment

• 2024/2025 1st module
  0.5 * Group presentation + 0.2 * HW1 + 0.3 * HW2 (group)

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

• Andreianov Pavel Pavlovich
• MAMEDLI MARIAM OKTAEVNA