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Regular version of the site
Bachelor 2021/2022

Machine learning in Economics and Finance

Type: Elective course (Economics)
Area of studies: Economics
When: 4 year, 1, 2 module
Mode of studies: distance learning
Open to: students of one campus
Instructors: Stepan Gogolev
Language: English
ECTS credits: 5

Course Syllabus

Abstract

The goal of mastering the discipline "Machine Learning for Business Analytics" 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

Learning Objectives

  • Know the basic problem statements, models and methods of machine learning.
  • Be 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

Expected Learning Outcomes

  • 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.
Course Contents

Course Contents

  • Supervised learning
  • Unsupervised learning
Assessment Elements

Assessment Elements

  • non-blocking Online course
    Completion of the online courses assigned by the teacher on the datacamp.com platform, performed in electronic format within the deadline set by the teacher.
  • non-blocking Homework
    Written report in electronic format assigned by the teacher completed in paris or alone. It should be finished and sent by e-mail to the teacher before the deadline set by the teacher. Report includes answers to the questions on the topics of appropriate online-courses.
  • non-blocking Exam
    Written report in electronic format completed in pairs or alone. It should be finished and sent by e-mail to the teacher before the deadline set by the teacher (day of the exam). Report includes answers to the list of questions provided to students no later than two weeks before exam.
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.25 * Homework + 0.5 * Exam + 0.25 * Online course
Bibliography

Bibliography

Recommended Core Bibliography

  • 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

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