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Basics of Artificial Intelligence and Machine Learning (SZF_167)

About Study Course

Department: Faculty of Social Sciences
ECTS:5
Course supervisor:Nataliia Kinash
Study type:Part-Time, Full time
Course level:Master's
Target audience:Management Science
Language:English
Study course description Full description, Part-Time, Full time
Branch of science:Electrical engineering, Electronic engineering, Information engineering; Other Sub-Branches of Electrical Engineering, Electronics, Information and Communication Technology

Objective

The student will learn to apply AI/ML techniques to solve business problems, perform data analysis, build predictive models, and make data-driven decisions while ensuring ethical AI use.

Prerequisites

No prior experience in AI or machine learning is required. However, a basic understanding of math, especially linear algebra, statistics, and probability, is helpful. Familiarity with Python programming (e.g., variables, loops, functions) and working with structured data (like spreadsheets or CSV files) will support course engagement. Strong analytical skills and an interest in problem-solving are also beneficial.E.g.:https://www.udacity.com/course/introduction-to-python--ud1110https://ww…

Learning outcomes

Knowledge

1.Explain key algorithms in supervised and unsupervised learning, including regression, classification, clustering, and ensemble methods.

Skills

1.Perform exploratory and visual data analysis and basic data preprocessing techniques.

Competence

1.Interpret model outputs and metrics to make data-driven business decisions.

Study course planning

Planning period:Year 2026, Spring semester
Study programmeStudy semesterProgram levelStudy course categoryLecturersSchedule
Digital Strategy and Artificial Intelligence Management2Master'sRequiredNataliia Kinash
Digital Strategy and Artificial Intelligence Management 2Master'sRequiredNataliia Kinash
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