This project is about the training and evaluation of different classifiers and explainable AI approaches on a medical data set. Specifically, the data consists of medical life data (EKG, ...) and class labels in the form of (partial) diagnoses or interventions. The aim is to identify the best classifiers that, on the one hand, make the fewest mistakes in the classification of test data and, on the other hand, provide explanations that are easy to understand and provide plausible justifications for the classification. Expert advice from a medical doctor for the evaluation can be made possible.
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-GP Grundlagenprojekt Intelligente Systeme | Gruppenprojekt | unbenotete Prüfungsleistung
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Studieninformation |
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