Advanced Machine Learning for Anomaly Detection [[AdvMLAD]]

Dozentinnen/Dozenten:

Bernhard Kainz, Johanna Müller

Angaben:

Seminar, 2 SWS, für FAU Scientia Gaststudierende zugelassen

Termine:

Zeit/Ort n.V.

Studienrichtungen / Studienfächer:

WPF DS-MA-AI 1

How to teach programming. [TPROG]

Angaben:

Übungsseminar, 2 SWS, für FAU Scientia Gaststudierende zugelassen

Kolloquium Machine Learning for Health Data Science [Koll-MLHDS]

Dozent/in:

Bernhard Kainz

Angaben:

Kolloquium, 3 SWS, für FAU Scientia Gaststudierende zugelassen

Termine:

Fr, 12:00 - 15:00, Zoom-Meeting

Inhalt:

This colloquium is a discussion platform for researches and students interested in machine learning research and image analysis. Our approach is focused on the discussion of recent developments and research papers and students' own research results. This aims at the improvement of presentation and conversation skills and other aspects for example career development, ethics and unconscious bias. The colloquium is open to all interested researchers and students. We will meet mainly on zoom but will also have personal meetings at the various locations of the research group.

Kolloquium Normative Representation Learning [KollNormLearn]

Dozent/in:

Bernhard Kainz

Angaben:

Vorlesung, 2 SWS, für FAU Scientia Gaststudierende zugelassen

Termine:

Mi, 17:00 - 19:00, Zoom-Meeting

Inhalt:

This colloquium is a discussion platform for researches and students interested in normative machine learning. Our approach is focused on the discussion of recent developments and research papers and students' own research results. This aims at the improvement of presentation and conversation skills and other aspects for example career development, ethics and unconscious bias. The colloquium is open to all interested researchers and students. We will meet mainly on zoom/MS Teams but will also have personal meetings at the various locations of the research group.

Medizintechnik II [MT2]

Dozentinnen/Dozenten:

Florian Knoll, Bernhard Kainz

Angaben:

Vorlesung, 4 SWS, ECTS: 3,75, für Anfänger geeignet, für FAU Scientia Gaststudierende zugelassen, This course will be held online. The blackboard exercises will start in the first (!) week (29.4) and the computer exercises in the third week (9.5-13.5). Old lecture videos can be found at https://www.video.uni-erlangen.de/course/id/1022 and all further information can be found on StudOn https://www.studon.fau.de/crs4223415.html

Studienrichtungen / Studienfächer:

PF MT-BA 2

Inhalt:

VORL; 4 SWS; guest listeners; Prof. Dr. Florian Knoll , Prof. Dr. Bernhard Kainz The lecture MT2 is aimed at students of medical technology and is one of the basic lectures in the field of informatics. Methods and devices that process and display the anatomy and function of the body for diagnosis and therapy are explained. Emphasis is placed on understanding and applying basic algorithms of medical imaging, such as segmentation, filtering, and image reconstruction. Modalities presented include X-ray systems, computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ultrasound (US).

StudOn: https://www.studon.fau.de/crs4223415.html

Empfohlene Literatur:

• Course Script: https://link.springer.com/book/10.1007/978-3-319-96520-8

• Arnulf Oppelt: Imaging Systems for Medical Diagnostics, Publicis Kommunikations AG, Erlangen, 2005

• Medical Imaging Systems - An Introductory Guide (Open Access) https://link.springer.com/book/10.1007%2F978-3-319-96520-8

Medizintechnik II Rechnerübung [MT2-RUE]

Dozentinnen/Dozenten:

Johanna Müller, Florian Knoll, Marc Vornehm, Jinho Kim, Mischa Dombrowski, Bernhard Kainz

Angaben:

Übung, 2 SWS, für Anfänger geeignet, für FAU Scientia Gaststudierende zugelassen, This course will be held in person. The exercises will start in the third week (9.-.13.5.)

Studienrichtungen / Studienfächer:

PF MT-BA 2

Inhalt:

In selbstständiger, aber betreuter Projektarbeit werden die Inhalte der Vorlesung direkt angewandt und dadurch vertieft. Dazu erarbeiten die Studierenden eine technische Lösung für eine konkrete medizinische Fragestellung.
Anmeldung zu den Rechnerübungen erfolgt ab dem 29.Apr 2022, 14:00 Uhr via Studon.

Medizintechnik II Tafelübung [MT2-TUE]

Angaben:

Übung, 2 SWS, ECTS: 1,25, für Anfänger geeignet, für FAU Scientia Gaststudierende zugelassen, This course will be held in person. Please note that the first meeting will be on Friday 29.4.

Studienrichtungen / Studienfächer:

PF MT-BA 2

Inhalt:

In selbstständiger, aber betreuter Projektarbeit werden die Inhalte der Vorlesung direkt angewandt und dadurch vertieft. Dazu erarbeiten die Studierenden eine technische Lösung für eine konkrete medizinische Fragestellung in gemeinsamer Gruppenarbeit.

Medizintechnik II Tutorenbesprechung [MT2-TUT]

Dozentinnen/Dozenten:

Johanna Müller, Nora Gourmelon, Florian Knoll, Bernhard Kainz

Angaben:

Übung, 2 SWS

Termine:

Fr, 8:15 - 9:45, 00.151-113

Project Representation Learning [PRL]

Dozentinnen/Dozenten:

Bernhard Kainz, Johanna Müller, Mischa Dombrowski

Angaben:

Sonstige Lehrveranstaltung, 8 SWS, ECTS: 10, nur Fachstudium

Termine:

Zeit/Ort n.V.

Studienrichtungen / Studienfächer:

WPF AI-MA ab 1
WPF MT-BA-BV ab 1
WPF INF-BA ab 1
WPF DS-MA-DW ab 1

Voraussetzungen / Organisatorisches:

recommended:
Deep Learning ML Prof. Dr. Andreas Maier 2+2 5 x E
Pattern Recognition ML Prof. Dr. Andreas Maier 3+1+2 5 x E
Maschinelles Lernen für Zeitreihen ML Prof. Eskofier, Prof. Oliver Amft, Dr. Ch. Mutschler 2+2+2 7.5 x E

Inhalt:

Different projects in the area of (deep) representation learning are on offer. These reach from theoretical exploration of new data representation methods to practical evaluation of applications in, e.g., medical image analysis. Example projects will be made available on the website of the IDEA Lab https://idea.tf.fau.eu/. Students may also propose their own projects, which will be coordinated and refined with the module lead during preliminary discussions.

Empfohlene Literatur:

A specific reading list will be established at the beginning of each project, general literature is listed below:
Quinn J, McEachen J, Fullan M, Gardner M, Drummy M. Dive into deep learning: Tools for engagement. Corwin Press; 2019 Jul 15. https://d2l.ai/
Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18. https://www.deeplearningbook.org/