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Advanced Machine Learning (SZF_168)
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:Business Management; 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
By the end of this course, students will learn to apply advanced machine learning techniques, including neural networks, LLMs, reinforcement learning, GANs, and autoencoders, to solve business problems. They will learn to build, deploy, monitor, and explain models, ensuring ethical use and strategic business impact.
Prerequisites
"Fundamentals of artificial intelligence and machine learning" course or equivalent.
Learning outcomes
Knowledge
1.Explain advanced machine learning concepts, models, and algorithms, including their theoretical foundations and practical implications.
Skills
1.Build and fine-tune advanced machine learning models
Competence
1.Choose and apply machine learning solutions to improve business decision-making and efficiency.
Study course planning
Planning period:Year 2026, Spring semester
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required | Nataliia Kinash | |
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required | Nataliia Kinash |
