Computer Science

This course covers image representation in frequency domain, Fourier transform, sampling theorem, Filtering, Wiener Filter, image enhancement, edge detection, Hough transform, segmentation, interest operators, mathematical morphology, vectorization, texture, sceletonization, medical axis and distance transform, contour/line tracing and -smoothing, Gestalt psychology, and grouping.

The course begins by writing a Python environment build while teaching the structure and use of various syntaxes. The course introduces various basic knowledge in Python one by one, arranging exercises in various situations at the same time so that students can accumulate the syntax and skills of writing Python programs while solving imaginary problems. The course content is introduced in the following order: 1. Python and authoring tools/platforms; 2. Python basic variable types, grammatical structure and package usage, and 3. Some of the most popular packages in Python. At the end of the course, students will choose a topic for a final project report (individual or group); students will be expected to introduce the problems they encountered and want to solve; how to solve these problems through Python, and present the results in visual ways.

This course provides a formal and practical introduction to the algorithms and data structures that underlie all areas of computation. It provides students with a toolbox of standard algorithms and data structures, as well as the skills to analyze both the theoretical complexity of algorithms and their practical behavior. Both written and programming exercises will be used, with examples from all areas of Informatics.

This course explores the theory and practice of software architecture as applied to the development of software systems, including enterprise systems, mobile applications, service-based systems, and microservices.