


Introduction to Computer Vision
A detailed introduction to computer vision will be made: image/video sampling, Image and video acquisition, Camera geometry, Stereo and Multiview imaging, Structure from motion, Structure from X, 3D Robot Localization and Mapping, Semantic 3D worl


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”.



Introduction to Tropical Geometry and its Applications to Machine Learning
Tropical geometry is a relatively recent field in mathematics and computer science combining elements of algebraic geometry and polyhedral geometry.



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.



Image Typology
This lecture overviews various digital image types: 2D images, 3D images (videos, medical volumes, hyperspectral images). Multichannel images, e.g., colour and multispectral images come next. RGBD images and graphics texture images.



Image Sampling
This lecture overviews spatial image frequency content and image sampling. Rectangular and hexagonal sampling grids are presented. Sampled image frequency content is analyzed and a 2D version of Shannon theorem is presented.