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xAI-Proj-M: Masterprojekt Erklärbares Maschinelles Lernen- Robust ML for real-world challenges- Dozent/in
- Francesco Di Salvo
- Angaben
- Projektseminar
Rein Präsenz
4,00 SWS, Unterrichtssprache Englisch
Zeit und Ort: Mi 10:00 - 14:00, WE5/01.006
- Englischsprachige Informationen:
- Title:
- xAI-Proj-M: Master Project Explainable Machine Learning- Robust ML for real-world challenges
- Credits: 6
- Prerequisites
- Degree Program: M.Sc. AI, M.Sc. CitH, M.Sc. WI
Requirements: Successfully passed the exam xAI-DL-M, xAI-MML-M, KogSys-ML-M or AI-KI-B (Introduction to AI)
Beneficiaries: Knowledge in programming (Python), Hands-on knowledge in machine learning and deep learning, scientific writing, LaTeX.
Registration: Email (in English, please) with name, matriculation number, degree program and completed ML-related modules to francesco.di-salvo@uni-bamberg.de before 21.04.
Initial meeting: 17/04/24 (Q&A and preliminary overview - not mandatory).
Kick-off: 24/10/24 (mandatory for participants).
- Contents
- Machine learning has become increasingly popular in recent years, with its applications in healthcare, finance, energy, and many other sectors. However, there are still several critical challenges that need to be addressed before these models can be safely and robustly adopted for widespread use.
The goal of this project is to develop robust machine learning algorithms that can perform reliably in challenging real-world scenarios. Working in teams of 3/4, students will have the opportunity to address some of the open problems in the field, including interpretability, model uncertainty, model efficiency, and data efficiency. After understanding the challenges and limitations of the chosen topic through the assigned paper(s), students will formulate, investigate, and validate their research questions under the guidance of the instructor.
Finally, the students will present their results to their peers and submit a technical report describing the ideas, methods, and results.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 20
- Institution: Lehrstuhl für Erklärbares Maschinelles Lernen
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