Seminar Advanced Deep Learning (SemADL)- Dozentinnen/Dozenten
- Prof. Dr.-Ing. Katharina Breininger, Dr.-Ing. Vincent Christlein, Prof. Dr.-Ing. habil. Andreas Maier
- Angaben
- Seminar
Online
2 SWS, ECTS-Studium, ECTS-Credits: 5
nur Fachstudium, Sprache Englisch, The first session of the semester will an online meeting, subsequent meetings will be in person.
Zeit: Di 12:00 - 14:00, Seminarraum ZMPT
- Studienfächer / Studienrichtungen
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Voraussetzungen / Organisatorisches
- Registration via StudOn:
https://www.studon.fau.de/crs4006742.html
https://www.studon.uni-erlangen.de/univis_2022s.Lecture.21733718
- Inhalt
- Deep Learning-based algorithms showed great performance in many fields of image processing and pattern recognition and compete with technologies such as compressive sensing and iterative optimization. The basis for the success of these algorithms is the availability of large amounts of data (big data) for training and of high computing power (typically GPUs).
In this seminar we try to explore advanced deep learning methods. In particular, we will aim to develop a deeper understanding of certain topics, for example: graph neural networks, unsupervised learning, differentiable learning, invertible learning, neural ordinary differential equations, transfer learning, multi-task learning, uncertainty DL, etc.
- ECTS-Informationen:
- Credits: 5
- Zusätzliche Informationen
- Schlagwörter: algorithms; medical image processing
Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 10
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2022/2023:
- Seminar Advanced Deep Learning (SemADL)
|