Machine Learning in Economics

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент экономики

Статус:

Курс обязательный

Когда читается:

2-й курс, 1, 2 модуль

Преподаватель

Сысоев Дмитрий Сергеевич

Course Syllabus

Abstract

Using data to make predictions, test hypotheses and estimate models is an important skill in the current job market. Many companies collect a lot of data and their decisions data-driven. Machine learning disrupts many fields and promises to achieve superhuman performance in the coming decades. Statistical analysis allows to test hypotheses and verify which of the models fits the data best. In this course we will cover different methods for supervised and unsupervised learning to develop a necessary toolkit for successful data scientists. For some of the methods we will go into details to learn why and how they work. Also we will touch on ethical implications of data science in the age of big data and apply learned methods to real business data sets.

Learning Objectives

• Students will free comfortable orienting among different methods of machine learning and develop a feeling of why these methods work and to extend them

Expected Learning Outcomes

• Understand the concept of data generating process and how it is different to the concept of model
• Learn more details on hypothesis testing
• Understand different methods for supervised learning such as regressions, random forest, gradient boosting etc.

Course Contents

• Python for data science
• Supervised machine learning
• Unsupervised machine learning
• Machine learning principles: cross-validation, feature selection, metrics
• Tools for data science

Assessment Elements

• Kaggle competition
• Exam
• Assignment

Interim Assessment

• 2024/2025 2nd module
  0.3 * Assignment + 0.5 * Exam + 0.2 * Kaggle competition

Bibliography

Recommended Core Bibliography

• Silver, N. (2012). The Signal and the Noise : Why So Many Predictions Fail-but Some Don’t. New York: Penguin Books. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1122593

Recommended Additional Bibliography

• Bruce E. Hansen. (2013). Econometrics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0DB9E1E

Authors

• BRODSKAYA NATALYA NIKOLAEVNA