xAI - Master- und Doktorandenseminar [xAI-MDSem]

Dozentinnen/Dozenten:

Christian Ledig, Sebastian Dörrich

Angaben:

Seminar, 2 SWS

Termine:

Do, 15:00 - 17:00, Online-Meeting

Voraussetzungen / Organisatorisches:

zur Voranmeldung und bei Interesse bitte email an christian.ledig@uni-bamberg.de.
Zielgruppe: Studierende im Master mit laufenden oder Interesse an zukünftigen Masterarbeiten am Lehrstuhl. Studierende mit generellem Interesse an aktuellen Forschungsfortschritten.

Inhalt:

Forum zur Diskussion von laufenden und zukünftigen Master- bzw. Promotionsthemen, sowie Forschungsprojekten und Forschungstrends im internationalen Umfeld. Möglichkeit zum Austausch und Networking mit Studierenden der FAU Erlangen-Nürnberg und dem Imperial College London.

xAI-DL-M: Deep Learning, Gruppe 1

Dozentinnen/Dozenten:

Sebastian Dörrich, Christian Ledig

Angaben:

Übung, 2,00 SWS

Termine:

Mi, 10:00 - 12:00, WE5/04.003

xAI-DL-M: Deep Learning, Gruppe 2

Dozent/in:

Sebastian Dörrich

Angaben:

Übung, 2,00 SWS

Termine:

Do, 8:00 - 10:00, WE5/04.003

xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen

Dozentinnen/Dozenten:

Sebastian Dörrich, Christian Ledig

Angaben:

Seminar, 2,00 SWS, ECTS: 3

Termine:

Mo, 16:00 - 18:00, WE5/05.005

Voraussetzungen / Organisatorisches:

Interest and registration
Registration via central registration or email to sebastian.doerrich@uni-bamberg.de (mailto: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)