News
Paper accepted: 19th IEEE International Conference on Advanced Robotics and Its Social Impacts
Anne Rother's paper has been accepted in the 19th IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO 2023).
Anne Rother, Gunther Notni, Alexander Hasse, Benjamin Noack, Christian Beyer, Jan Reißmann, Chen Zhang, Marco Ragni, Julia Arlinghaus, and Myra Spiliopoulou. "Productive teaming under uncertainty: when a human and a machine classify objects together".
More information: https://ieee-arso.org/
Dates for LAST ATTEMPT exams
The date for the LAST ATTEMPT exams are:
17.07.2023
- 10:00
- 10:30
- 11:00
- 14:00
- 14:30
- 15:00
NOTE: Last attempt exams are offered exclusively for degrees that prescribe a last attempt, and state that this attempt must be an oral exam.
Important announcement regarding registration process:
In order to stay more compliant to the social distancing guidelines, the registration for the exam will be conducted slightly differently.
Download, print, and fill out the examination registration form from the examination office. Fill all fields except the date and time.
Submit the document to Mr. Knopke electronically Deadline 01.07.2023.
The next available time slot will be assigned to you, and Mr. Knopke forwards the updated form to the examination office. You will be informed about the date and time for your exam.
Invited talk in the Scientific Colloquium KMD
Summer Term 2023, April 5, 9:40 a.m.
Title of the presentation: Explaining Drug Recommendations with Deep Learning
By: Panagiotis Symeonidis
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Zoomroom access data:
https://ovgu.zoom.us/j/67437141415
Meeting-ID: 674 3714 1415
Kenncode: 951623
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DM4BA verlegt - Übergangslösung SoSe 23
Die Veranstaltung "Data Mining – Einführung in Data Mining" (DM4BA) für Bachelor wurde vom Sommersemester ins Wintersemester verlegt. Die zugehörige Vorlesung wird daher auch nur im Wintersemester angeboten.
Studierende, die im Sommersemester 2023 die DM4BA belegen wollten, dürfen an die englischsprachige DM I Vorlesung teilnehmen; es wird eine deutschsprachige Übung angeboten, exklusiv für diese Studierende. Dieser findet vom 20.04.2023 bis 13.07.2023 wöchentlich donnerstags von 11:00 bis 13:00 Uhr in Raum G22A-210 statt. Die Anzahl der Plätze ist auf maximal 24 begrenzt. Es wird jedoch dringend empfohlen, den Kurs während des Wintersemesters vollständig zu besuchen. Weitere Einzelheiten werden in der ersten Übung besprochen.
Achtung! Bachelorstudierende dürfen sich nicht für die DM I Prüfung anmelden; Anmeldung zur DM4BA Prüfung wird auch im Sommersemester möglich sein.
Inspection of Examinations / Klausureinsicht
Exam inspections / Klausureinsicht:
All students who wish to inspect their exams from the previous semester can do so on the 26th of April, 2023. Those of you who want to inspect your exam are requested to write to the person responsibe for your course in order to arrange a personal appointment.
| Course | Contact Person |
| Data Mining I & II | Vishnu Unnikrishnan |
| DM4BA | Miro Schleicher |
| ITO | Maik Büttner |
| Recommenders | Noor Jamaludeen |
| WMS | Christian Beyer |
An appointment is required to arrange an exam inspection. The appointments will be offered on 26th April, 2023, and are non-transferable.
Tutorial Mining and multimodal learning from complex medical data
The proliferation of medical data and applications has increased the need for extracting useful knowledge that can be effectively used by the healthcare domain experts. The motivation of this tutorial is to address the complexity of medical data with specific focus on their temporal nature. While earlier tutorials in both AIME as well as other related venues such as KDD and ECML/PKDD have explored the application and utility of machine learning on medical data, there has yet been limited focus on the challenges emerging from the sequential and temporal nature of such data, as well as on the need for trust by the medical practitioners.
'Best paper award' at AIME 2022
Miro Schleicher has received the Marco Ramoni best paper award at the 20th Artificial Intelligence in Medicine (AIME) conference for his paper 'When can I expect the mHealth user to return? Prediction meets time series with gaps' (Miro Schleicher, Rüdiger Pryss, Winfried Schlee and Myra Spiliopoulou).
This work is within the frame of the UNITI project that encompasses machine learning methods for choosing the best treatment for each tinnitus patient. Treatments have an mHealth component, which assists the users towards self-empowerment and daily management of their disease. However, mHealth apps demand self-discipline; some users give up or interact very irregularly. The proposed method learns from the data of each user and from the absence of data, and it predicts if and when a user will start interacting again with the app.
