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Бакалавриат 2019/2020

Машинное обучение для бизнес-аналитики

Статус: Курс по выбору (Экономика)
Направление: 38.03.01. Экономика
Когда читается: 4-й курс, 2, 3 модуль
Формат изучения: Blended
Язык: английский
Кредиты: 5

Программа дисциплины

Аннотация

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.
Цель освоения дисциплины

Цель освоения дисциплины

  • 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.
Результаты освоения дисциплины

Результаты освоения дисциплины

  • 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.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • 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.
Элементы контроля

Элементы контроля

  • неблокирующий Created with Sketch. Online course
  • неблокирующий Created with Sketch. Homework
  • неблокирующий Created with Sketch. Exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (3 модуль)
    0.51 * Exam + 0.19 * Homework + 0.3 * Online course
Список литературы

Список литературы

Рекомендуемая основная литература

  • Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286

Рекомендуемая дополнительная литература

  • Knox, S. W. (2018). Machine Learning : A Concise Introduction. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1729639