Delivered at:: School of Economics and Finance

Course type:: Compulsory course

When:: 4 year, 1, 2 module

Instructor

Kashin, Dmitry

Full Syllabus

Abstract

The goal of mastering the discipline "Machine learning in Economics and Finance" is to familiarize students with the theoretical foundations and basic principles of machine learning, as well as to develop students' practical skills in working with data and solving applied problems of data analysis. This discipline belongs to the cycle “Variable part of the profile”, specialization “Business analytics and applied economics”. Type of a course: with online course.

Learning Objectives

Know the basic problem statements, models and methods of machine learning.
Able to apply algorithms for estimating model parameters and building forecasts.
Have skills in identifying machine learning methods appropriate for the research objective.
Have skills in identifying statistical outliers and filling in missing values.

Expected Learning Outcomes

Can identify a problem suitable for machine learning
Analyze data with machine learning tools
Be able to use the scikit-learn library to train machine learning models.
Able to formulate a statement of the problem according to the proposed data. Selects a suitable forecast model for the existing task. Knows basic tests for comparing model quality. Able to use tests to select the most suitable model for the task.
Able to formulate the statement of the problem of teaching without a teacher according to the proposed data. Selects the appropriate teaching method for the existing task. Knows basic tests for selecting adequate methods and searching for hyperparameters.
Apply basic machine learning tools

Course Contents

Supervised learning
Unsupervised learning

Assessment Elements

ДКР
Домашняя контрольная работа по машинному обучению
Семинары
Экзамен
Онлайн-курс
Пройти два онлайн-курса на платформе DataCamp

Interim Assessment

2024/2025 2nd module
0.3 * ДКР + 0.1 * Онлайн-курс + 0.2 * Семинары + 0.4 * Экзамен

Bibliography

Recommended Core Bibliography

A first course in machine learning, Rogers, S., 2012
Data mining : practical machine learning tools and techniques, Witten, I. H., 2011
Foundations of machine learning, Mohri, M., 2012
Introduction to machine learning, Alpaydin, E., 2020
Machine learning : the art and science of algorithms that make sense of data, Flach, P., 2014
Machine learning, Mitchell, T. M., 1997
Mohammed, Mohssen Khan, Muhammad Badruddin Bashier, Eihab Bashier Mohammed. Machine Learning: Algorithms and Applications. Auerbach Publications © 2017 // https://library.books24x7.com/toc.aspx?bookid=117434
Trevor Hastie, Robert Tibshirani , et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, 2017. Free from the publisher: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

Recommended Additional Bibliography

Lantz, B. (2013). Machine Learning with R : Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight Into Real-world Applications. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=656222
Matt Taddy. (2019). Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. McGraw Hill.

Authors

Борисова Елена Феликсовна
GOGOLEV STEPAN LEONIDOVICH
KASHIN DMITRIY VIKTOROVICH

Bachelor’s Programme 'Economics'

Machine learning in Economics and Finance