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Vorlesungsverzeichnis >> Fakultät Wirtschaftsinformatik und Angewandte Informatik >> Bachelor-/Masterstudiengänge Angewandte Informatik, Computing in the Humanities, International Information Systems Management, Software Systems Science, International Software Systems Science, Wirtschaftsinformatik, Wirtschaftspädagogik mit Schwerpunkt Wirtschaftsinformatik >>
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xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen- Dozent/in
- Prof. Dr. Christian Ledig
- Angaben
- Seminar
Rein Präsenz
2,00 SWS, Unterrichtssprache Deutsch
Zeit und Ort: Mi 14:00 - 16:00, WE5/03.004
- Inhalt
- Focus Topic in SS 2023: Evaluation of AI models
Motivation
In this seminar, we will focus on approaches for evaluating different kinds of AI models. The careful quantitative assessment of the performance of an AI model is of critical importance to ensure its safe deployment in real-world settings. It is further the foundation of scientific research, enabling researchers to assess the impact of algorithmic changes and compare the performance of an AI system to the state of the art. Most importantly, objective, thorough evaluation allows the identification of biases and weaknesses in the system that would be problematic in practice, potentially putting the user or patient at risk.
Goals and Topics
You will learn about established evaluation measures for different types of AI algorithms with a focus on the domain of computer vision and medical image processing. Specifically you will learn how to evaluate algorithms for: image classification, object detection and image segmentation. You will further have the opportunity to learn about common pitfalls, data biases, ground truthing, cross-validation and important considerations when creating datasets for evaluation.
Time and location
Wednesdays (2-4pm) in WE5/03.004;
Initial Meeting (general info): 19.04;
Second Meeting (mandatory for participants): 26.04.
- Englischsprachige Informationen:
- Title:
- xAI-Sem-B1: Bachelor Seminar Explainable Machine Learning
- Credits: 3
- Institution: Lehrstuhl für Erklärbares Maschinelles Lernen
Hinweis für Web-Redakteure:
Wenn Sie auf Ihren Webseiten einen Link zu dieser Lehrveranstaltung setzen möchten, verwenden Sie bitte einen der folgenden Links:Link zur eigenständigen Verwendung Link zur Verwendung in Typo3
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