Machine Learning for Business

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент бизнес-информатики и операционного менеджмента

Статус:

Курс обязательный

Когда читается:

4-й курс, 1 модуль

Преподаватели

Ефимов Константин Дмитриевич

Омеляш Ксения Васильевна

Course Syllabus

Abstract

The average course completion time may vary depending on the student's initial training. The prerequisite for mastering the course is knowledge of mathematics at the secondary education level and the basics of the Python programming language. Students' academic success is assessed through programming assignments in the form of contests, as well as written controls in the form of tests. The final exam is a contest with a presentation of the solutions proposed by the students. With the help of course assignments, basic methods of data preprocessing are worked out. model construction, interpretation of results. The course does not involve lectures, all theoretical materials are provided to students in practical classes.

Learning Objectives

• • Understanding the basic rules of syntax, data types, and built-in constructs • Create custom preprocessing pipelines • Mastering the main Python machine learning library: sklearn • Formation of basic skills in using Python as a classification and forecasting tool

Expected Learning Outcomes

• the student is able to explain the main types of data and the formulation of the research task
• the student is able to create basic machine learning models for regression and classification tasks
• the student is able to find and eliminate syntactic and logical errors in scripts
• the student is able to analyze the results obtained in order to describe economic processes

Course Contents

• 1. Problem statement, data, pipelines, metrics
• 2. Linear regression models
• 3. Classification: logistic regression, imbalance
• 4. Trees and ensembles for tabular data
• 5. Interpretation, stability, drift
• 6. Time series for economists
• 7. Automatic

Assessment Elements

• Test
• Contest 1
  Contest 1 is a competition on the Kaggle website for students of the course. The students' task is to get the correct answers to the prediction tasks uploaded to the website. To get the correct answers to the prediction problem, the student must build a machine learning model by applying knowledge from the course topics. The number of attempts by the student is not limited until the end of the time of the control element. Students are prohibited from sharing solutions. After uploading, the student's result is reflected on the leaderboard.
• Contest 2
  Contest 2 is a competition on the Kaggle site for students of the course. The students' task is to get the correct answers to the prediction tasks uploaded to the website. To get the correct answers to the prediction problem, the student must build a machine learning model by applying knowledge from the course topics. The number of attempts by the student is not limited until the end of the time of the control element. Students are prohibited from sharing solutions. After uploading the solution, the student's result is reflected on the leaderboard.
• Project
  The students' task is to get the correct answers to the prediction tasks of their choosing. To get the correct answers to the prediction problem, the student must build a machine learning model by applying knowledge from the course topics. The number of attempts by the student is not limited until the end of the time of the control element. Students are prohibited from sharing solutions. Students submit solutions in .ipynb format for verification and prepare a presentation justifying the chosen methods for the solution. Students answer questions about their code during the exam. Students can take work in groups of up to 5 people.

Interim Assessment

• 2026/2027 1st module
  0.5 * Project + 0.1 * Test + 0.2 * Contest 1 + 0.2 * Contest 2

Bibliography

Recommended Core Bibliography

• Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019

Recommended Additional Bibliography

• Data mining : practical machine learning tools and techniques, Witten, I. H., 2011
• Text as Data: A New Framework for Machine Learning and the Social Sciences, Grimmer, J., 2022

Authors

• Orlova Ekaterina Dmitrievna