Computer Vision und Maschinelles Lernen 1

What it is about

You will learn the fundamentals of image acquisition, projective geometry and image processing as well as basic methods of machine learning. In the end you will be able to geometrically and in the frequency/spatial domain analyze images, implement simple CV operations and design and evaluate classical methods for classification and regression.

What you will be able to do

• Projective geometry and image acquisition understand
• Abstract geometric relationships in projective space
• Design and implement spatial-domain operations
• Apply image description and analysis using Fourier and Wavelet transforms
• Explain the fundamentals of machine learning
• Represent formal learning problems abstractly
• Design and evaluate ML processing pipelines
• Derive deterministic methods for classification and regression

What the module consists of

• LectureConveying the theoretical foundations of computer vision and machine learning
• Programming exercises in small groupsPractical implementation and consolidation of algorithms
• Student impulse talksDeepening individual topics and presentation practice
• Literature reviewIndependent engagement with professional literature

Teaching method

• Lectures by the instructorsExplanation of theory and concepts
• Programming exercises in small groupsPractical application and implementation
• Literature reviewDeepening and expansion of the lecture material
• Student impulse talksActive participation and presentation of individual topics