Study Guide

Materials

All the necessary course materials will be compiled and made available via the Moodle workspace.

Self-study prerequisites, if not familiar: Basics of Python:

Familiarity with the basic concepts and syntax of the Python programming language. Basics of Data Management: Understanding of fundamental aspects of data handling, including JSON and databases. Basics of Information Technology: Knowledge of key IT concepts, including understanding what a CPU and GPU are.

Teaching methods

Online material. Practical exercise support offered in workshops

Content scheduling

- Introduction to Artificial Intelligence and Machine Learning: Applications and Utilization Opportunities
- Machine Learning Algorithms and Methods, such as Regression Analysis, Decision-Making Algorithms
- Supervised and Unsupervised Classification Algorithms, Principal Component Analysis
- Application Examples and Project Work
- Machine Learning Platforms and Libraries

Evaluation scale

H-5

Assessment methods and criteria

Grades are based on the quality, quantity, and comprehensiveness of the exercises.