Objectives / course description: This course introduces the fundamental concepts and practical methods used in signal and image analysis from both research and industrial perspectives. These include essential techniques for analysing one-dimensional signals and two-dimensional images, with applications in biomedical engineering and digital health. Throughout the course, theoretical concepts are complemented by guided practical sessions and demonstrations based on real-world problems involving signal and/or image analysis. The students will gain hands-on experience implementing analysis methods using Python/MATLAB and related software tools.
The course emphasises both conceptual understanding and computational implementation, enabling students to develop practical skills. By the end of the course, students will be able to understand the core principles of signal and image analysis, apply standard processing techniques, interpret computational results, and independently implement basic analysis workflows.
Evaluation: Quizzes + Tutorials (homework, practical sessions) + Written Exam
Prerequisites: Followed a basic course in linear algebra. Knowledge of probability theory and statistics is a plus but not mandatory. Basic programming knowledge of Python or R to perform the practical activities.
