Basics of Artificial Intelligence and Machine Learning (SZF_167)
About Study Course
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
Not necessary.
Learning outcomes
1.- key algorithms in supervised and unsupervised learning, including regression, classification, clustering, and ensemble methods;
- basic understanding of model evaluation metrics and interpreting model results;
- practical knowledge of open-source AI/ML tools and libraries.
1.- performing exploratory and visual data analysis and basic data preprocessing techniques;
- building and evaluating machine learning models;
- deploying basic models.
1.- interpreting model outputs and metrics to make data-driven business decisions;
- integrating AI/ML models business workflows, aligning technological capabilities with organizational goals;
- address ethical considerations, such as bias and fairness, when applying AI/ML in business environments.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required | ||
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required |
