Bachelor
2025/2026
Machine Learning
Type:
Compulsory course (Digital Product Management)
Delivered by:
Department of Business Informatics
When:
3 year, 1 module
Open to:
students of one campus
Instructors:
Yury Sanochkin
Language:
English
ECTS credits:
4
Contact hours:
40
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
- HAAverage grade for all practical homework assignments provided in the course
- ExamExam is a practical work performed by students based on the results of mastering the course.
- ActivityAssessing student attendance and activity at seminars, as well as activity at lectures
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.