Machine Learning

2025/2026

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Department of Business Informatics

Course type:

Compulsory course

When:

3 year, 1 module

Instructor

Sanochkin, Yury

Course Syllabus

Abstract

It is said that automation increases productivity, which in turn drives global economy and helps to improve quality of life. For the last decade machine learning remains one of the key sources of automation in nearly all industries. This course familiarizes students with modern machine learning algorithms by both providing theoretical basis and hands-on experience with Python libraries.

Learning Objectives

• The course is aimed to provide students with necessary knowledge and tools to work with machine learning tasks.
• During the learning process, students will gain the ability to develop real ML projects and solve real tasks with the connection to business needs of the companies

Expected Learning Outcomes

• Be able to set up python environment for ML task;
• Understand key concepts of ML, current trends of AI;
• Be able to pass through all steps of DS task: EDA, process missing data and outliers, train an ML model, evaluate an ML model;
• Be able to find and read articles about ML applications.

Course Contents

• 1. Introduction to machine learning. Types of ML tasks and model classes.
• 2. Metrics. KNN. Naive Bayes
• 3. Regression. Linear Regression
• 4. Classification.
• 5. Trees. Ensemble of tries

Assessment Elements

• HA
  Average grade for all practical homework assignments provided in the course
• Exam
  Exam is a practical work performed by students based on the results of mastering the course.
• Activity
  Assessing student attendance and activity at seminars, as well as activity at lectures

Interim Assessment

• 2025/2026 1st module
  0.42 * Activity + 0.43 * Exam + 0.15 * HA

Bibliography

Recommended Core Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
• Pattern recognition and machine learning, Bishop, C. M., 2006

Recommended Additional Bibliography

• Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
• Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.

Authors

• Sanochkin Yuriy Ilich