Machine learning in Economics and Finance

2022/2023

Academic Year

ENG

Instruction in English

5

ECTS credits

Delivered at:

School of Economics and Finance

Course type:

Elective course

When:

4 year, 1, 2 module

Instructor

Gogolev, Stepan

Course 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

• Homework
  Written report in electronic format assigned by the teacher completed in paris or alone. It should be finished and loaded to LMS before the deadline.
• Online course
  Taking online courses, preparing its notes and confirming studying the material by passing the test, answering questions or preparing presentaton.
• Exam
  Written report in electronic format completed in pairs or alone. It should be finished and loaded to LMS before the deadline given by the teacher. Report includes answers to the list of questions provided to students no later than two weeks before exam.

Interim Assessment

• 2022/2023 2nd module
  0.42 * Exam + 0.29 * Online course + 0.29 * Homework

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.