

Socially Informed ML Practices
A series of three posts on “Is data fixable? On the need of socially-informed practices in ML research and education”.



Symbolic, Statistical, and Causal Representations
In machine learning, we use data to automatically find dependencies in the world, with the goal of predicting future observations.



Introduction to Image Processing
This lecture overviews digital images, image processing and image analysis. 1D signals, 2D signals (images), 3D signals (videos, medical volumes) are presented. Multichannel images, e.g., color and multispectral images come next.



Computational Geometry
This lecture overviews Computational Geometry that has many applications in Computer Graphics, Robotics, Geographic Information Systems, CAD/CAM.



Mathematical Morphology
This lecture overviews Mathematical Morphology that has many applications in digital image processing, analysis and computer vision.



Shape Description
This lecture overviews Shape Description that has many applications in object recognition and image compression. It covers the following topics in detail: Chain Codes. Polygonal Contour Approximations. Fourier Descriptors. Quadtrees.