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Regular version of the site

Machine Learning for Business Analytics

2019/2020
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Elective course
When:
4 year, 2, 3 module

Instructors

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 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
    Theme 1. Introduction to machine learning. Overview of various machine learning approaches. Approaches to validating the results. Theme 2. The problem of classification. Statement of the problem, analysis of the main methods, assessment of the quality of the model. Theme 3. The problem of regression. Statement of the problem, analysis of the main methods, assessment of the quality of the model. Theme 4. The problem of forecasting. Statement of the problem, analysis of the main methods, assessment of the quality of forecasting.
  • Unsupervised learning
    Theme 5. Problem of clustering. Statement of the problem, analysis of the main methods, assessment of the quality of clustering. Topic 6. The problem of treatment effect estimation. Statement of the problem, analysis of the main methods of assessment. Theme 7. The problem of outliers detection. Ways to find outliers. Reasons for the appearance of atypical observations. Work with atypical objects in a sample. Topic 8. The task of filling in the missing variables. Reasons for the appearance of atypical variables. Ways to recover missing variables.
Assessment Elements

Assessment Elements

  • non-blocking Online course
  • non-blocking Homework
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.51 * Exam + 0.19 * Homework + 0.3 * 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