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Code Repository for the paper ''Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation".

Code and result snapshot from my master thesis.

Vorlesungsfolien, Hexapawn und Jupiter Notebook zum Vortrag "Einführung Maschinelles Lernen - Vom traditionellen Programm zum Neuronen Netz"

This project contains code for the numerical computation of analytic deep prior solutions. There are four different ways which are compared.

This project is situated at the intersection of cognitive neuroscience and machine learning, with a focus on decoding auditory and visual attention through electroencephalography (EEG) data. Assigned as part of a brain pattern recognition course, our challenge is to classify the directions of visual and auditory attentions—left or right—utilizing EEG recordings. Participants underwent a series of tests, listening to stimuli while looking in specific directions. The generated dataset, comprised of raw EEG data and CSV files, is annotated with labels indicating the direction of both auditory and visual attentions.