Teaching

MIA002 - “Topics in Intelligent Systems”
This is essentially a Statistical Learning course. An introduction to Bayes decision theory, geometric interpretations and analytical derivations considering the Gaussian classifier (in multiple scenarios). Basic principles of Maximum-likelihood estimation. Mixture models and incomplete data. The EM algorithm.
[2025]
MEIC001 - “Machine Learning”
The Machine Learning techniques covered in this course are essentially focused on relational data on tabular formats. Knowledge extraction, outlier detection and feature engineering are among some of the topics discussed. Classification, clustering, regression. Online learning, frequent pattern mining.
[2025]
MVCOMP08 - “Advanced Image Processing Analysis”
In this course we cover advanced techniques for image processing. Image alignment, segmentation, enhancement/restoration (super-resolution, denoising, inpainting, coloring, i.e. image-to-image “translation”) and saliency.
[2025]
PRODEI040 - “Analysis of Social and Information Networks”
Many complex problems can be naturally represented as graphs, capturing relationships between objects. Such networks are fundamental for modeling social, technological, and biological systems. This course explores machine learning with graphs, focusing on the computational, algorithmic, and modeling challenges associated with analyzing large-scale graphs.
[2025]

LEIC029 - “Artificial Intelligence”
This course introduces the core concepts of Artificial Intelligence and Intelligent Systems, focusing on their foundations, methods, and real-world applications. Students learn to design AI agents and systems, apply problem-solving, learning, and reasoning techniques, and develop complete AI-based projects.
[2025]