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  xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen

Dozentinnen/Dozenten
Sebastian Dörrich, M.Sc., Prof. Dr. Christian Ledig

Angaben
Seminar
Rein Präsenz
2,00 SWS, Unterrichtssprache Englisch
Zeit und Ort: Mo 16:00 - 18:00, WE5/05.005 (außer Mo 27.1.2025)

Voraussetzungen / Organisatorisches
Interest and registration
Registration via central registration or email to sebastian.doerrich@uni-bamberg.de with matriculation number, degree program, completed ML-related modules before 18.10.2024.

Eligibility
B.Sc. KI & Data Science, B.Sc. AI, B.Sc. ISoSySc, (B.Sc. WI only after prior consultation with the examination office), (potentially also as MSc CitH course)

Requirements
None

VC-Course: https://vc.uni-bamberg.de/course/view.php?id=70919

Beneficiaries
Critical thinking, hands-on knowledge in machine learning and deep learning, fundamental aspects of medical imaging or healthcare technology, scientific writing and presenting, LaTeX

Inhalt
Topic:
Applications and Innovations of Machine Learning in Healthcare

Overview:
Advances in machine learning (ML) are revolutionizing healthcare, providing powerful tools to assist clinicians in diagnosing diseases and analyzing complex medical data. With the growing availability of large medical datasets and the development of sophisticated algorithms, ML has the potential to reshape clinical workflows, enhance diagnostic accuracy, and offer new insights into patient care. This seminar aims to equip students with a comprehensive understanding of both the technical methods and the broader impact of integrating ML into healthcare. Students will explore how ML is applied to tasks like disease detection, drug development, and robotic surgery, all of which are critical in modern healthcare. However, while these technologies hold great promise, their real-world application brings also significant challenges, such as the reliability of ML models, data privacy, and ethical concerns. Addressing these issues is essential for the safe and effective deployment of ML in clinical settings. Thus, this seminar will furthermore provide a multi-disciplinary perspective, encouraging students to critically assess both the technical innovations and the wider implications of AI in healthcare.

Initial meeting: 14/10/24 (General info, Q&A and preliminary overview - not mandatory)

Kick-off: 21/10/24 (mandatory for participants)

Englischsprachige Informationen:
Title:
xAI-Sem-B1: Bachelor Seminar Explainable Machine Learning

Credits: 3

Zusätzliche Informationen
Erwartete Teilnehmerzahl: 15

Institution: Lehrstuhl für Erklärbares Maschinelles Lernen

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