Stud.IP Uni Oldenburg
University of Oldenburg
13.08.2022 02:36:03
Veranstaltungsverzeichnis

School of Computing Science, Business Administration, Economics and Law Click here for PDF-Download

Winter semester 2022/2023 17 Seminars
VAK Course Number Title Type Lecture
Preliminary studies
Advanced courses
Practical course
Colloquium
Research group
Workgroup
Project group
Council conference
Internship
Language course
Subject didactics
Excursion
Tutorial
Committee
SWS Semester weekly hours Teachers Degree
2.01.369-B Selected topics in nanomechanics and the mechanical characterization of nanomaterials using microscopy Tuesday: 14:15 - 15:45, weekly (from 18/10/22)

Description:
Seminar 2 Dr. James Mead
  • Master
2.01.973 Psychological practicum fNIRS, EEG The course times are not decided yet.
Description:
The aim of the internship is to apply the knowledge from the lecture on Neurophysiology in the lab. For this purpose, the students will record electroencephalograms from human subjects. They will learn how to place electrodes, how to record EEG and how to analyze simple derivatives of EEG such as the event-related potential (ERP). In the Applied Cognitive Neurocognitive Psychology lab interns are closely linked to ongoing research. The research topics in the lab include characterization and decoding of cognitive and emotional states from brain imaging data (fNIRS, fMRI, MEG) and the analysis of mechanisms of auditory processing of speech. In both topical areas we put a methodological emphasis on data driven machine learning techniques to reveal neuronal correlates of internal states and for human state decoding. In collaboration with workgroups from computer science we also work modelling of human cognitive function and integration of information about human cognitive and emotional states into planning strategies for human-cyber-physical systems. The aim of the internship is to apply the knowledge from the lecture on Neurophysiology in the lab. For this purpose, the students will record electroencephalograms from human subjects. They will learn how to place electrodes, how to record EEG and how to analyze simple derivatives of EEG such as the event-related potential (ERP). In the Applied Cognitive Neurocognitive Psychology lab interns are closely linked to ongoing research. The research topics in the lab include characterization and decoding of cognitive and emotional states from brain imaging data (fNIRS, fMRI, MEG) and the analysis of mechanisms of auditory processing of speech. In both topical areas we put a methodological emphasis on data driven machine learning techniques to reveal neuronal correlates of internal states and for human state decoding. In collaboration with workgroups from computer science we also work modelling of human cognitive function and integration of information about human cognitive and emotional states into planning strategies for human-cyber-physical systems.
Practical training - Prof. Dr. habil. Christoph Siegfried Herrmann, Dipl.-Ing.
Prof. Dr. Jochem Rieger
  • Master
2.01.368 Microrobotics: Selected Topics Tuesday: 10:15 - 11:45, weekly (from 18/10/22), S

Description:
Seminar 2 Prof. Dr. Sergej Fatikow
  • Master
2.01.813-A Current Topics in Interpretable/Explainable AI (XAI) Thursday: 10:15 - 11:45, weekly (from 20/10/22)

Description:
This seminar will cover different aspects of interpretable/explainable AI (XAI) ranging from inherently interpretable models over perturbation-based methods, such as Shapley values, to gradient-/decomposition-based approaches and their quantitative evaluation. Going beyond conventional single-feature attribution methods, we will also discuss current concept-based attribution methods, ways to assess feature interactions and connections to causality. This seminar will cover different aspects of interpretable/explainable AI (XAI) ranging from inherently interpretable models over perturbation-based methods, such as Shapley values, to gradient-/decomposition-based approaches and their quantitative evaluation. Going beyond conventional single-feature attribution methods, we will also discuss current concept-based attribution methods, ways to assess feature interactions and connections to causality.
Seminar 2 Prof. Dr. Nils Strodthoff
Juan Lopez Alcaraz
  • Master
2.01.812-B Selected Topics in IT-Security Thursday: 16:15 - 17:45, weekly (from 20/10/22)

Description:
Seminar 2 Prof. Dr. Andreas Peter
  • Master
2.01.174 Virtual and Augmented Reality Lab Monday: 14:15 - 17:45, weekly (from 17/10/22), PR

Description:
Die Veranstaltung „Augmented, Mixed, and Virtual Reality“ im Modul inf174 (“Spezielle Themen der Medieninformatik und Multimedia Systeme“) führt grundlegende Technologien und Paradigmen der Augmented Reality (AR), Mixed Reality (MR) und Virtual Reality (VR) ein. In den ersten Wochen lernen die Studierenden so Methoden des Trackings, der 3D/2D Anzeige und Interaktion mit virtuellen Objekten sowie aktuelle Anzeigegeräte (z.B. Microsoft Hololens, https://www.microsoft.com/de-de/hololens) und Software (z.B. Unity) kennen und lernen diese in praktischen Übungsaufgaben kennen. In einem praktischen Projekt ab Woche 3 werden die Studierenden in Kleingruppen (~3) ein Mini-Forschungsprojekt im Themenkomplex AR/MR/VR planen und umsetzen. Die Studierenden erarbeiten dabei auf Basis der aktuellen Forschungslage eine Forschungsfrage, die sie mit Hilfe des zu entwickelnden Prototypen beantworten möchten. An die Planungsphase schließt sich eine ca. 6-wöchige Entwicklungsphase an, die mit der Evaluation des Prototypen endet. Die Ergebnisse des Projektes in einer Abschlusspräsentation vorgestellt. Die Veranstaltung „Augmented, Mixed, and Virtual Reality“ im Modul inf174 (“Spezielle Themen der Medieninformatik und Multimedia Systeme“) führt grundlegende Technologien und Paradigmen der Augmented Reality (AR), Mixed Reality (MR) und Virtual Reality (VR) ein. In den ersten Wochen lernen die Studierenden so Methoden des Trackings, der 3D/2D Anzeige und Interaktion mit virtuellen Objekten sowie aktuelle Anzeigegeräte (z.B. Microsoft Hololens, https://www.microsoft.com/de-de/hololens) und Software (z.B. Unity) kennen und lernen diese in praktischen Übungsaufgaben kennen. In einem praktischen Projekt ab Woche 3 werden die Studierenden in Kleingruppen (~3) ein Mini-Forschungsprojekt im Themenkomplex AR/MR/VR planen und umsetzen. Die Studierenden erarbeiten dabei auf Basis der aktuellen Forschungslage eine Forschungsfrage, die sie mit Hilfe des zu entwickelnden Prototypen beantworten möchten. An die Planungsphase schließt sich eine ca. 6-wöchige Entwicklungsphase an, die mit der Evaluation des Prototypen endet. Die Ergebnisse des Projektes in einer Abschlusspräsentation vorgestellt.
Lecture 2 Prof. Dr. techn. Susanne Boll-Westermann
M. Sc. Tobias Lunte
M. Sc. Mikolaj Wozniak
  • Master
2.01.805 Introduction to IT-Security Monday: 16:15 - 17:45, weekly (from 17/10/22), Location: V04 0-033
Wednesday: 14:15 - 15:45, weekly (from 19/10/22), Location: A04 2-221
Thursday: 10:15 - 11:45, weekly (from 20/10/22), Location: A04 2-221

Description:
Lecture 6 Prof. Dr. Andreas Peter
  • Bachelor
  • Master
2.01.801-A Forschungsseminar Sicherheit und Erklärbarkeit Lernender Systeme The course times are not decided yet.
Description:
Seminar - Prof. Dr. Daniel Neider
  • Bachelor
2.01.597 Practical Deep Learning in PyTorch Tuesday: 14:15 - 15:45, weekly (from 18/10/22), V
Friday: 10:15 - 11:45, weekly (from 21/10/22)

Description:
This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study. This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study.
Lecture 4 Prof. Dr. Nils Strodthoff
Juan Lopez Alcaraz
  • Master
2.01.456 Real-Time-Systems Wednesday: 10:15 - 11:45, weekly (from 19/10/22), Location: A05 1-160
Thursday: 14:15 - 15:45, weekly (from 20/10/22), Location: A01 0-006

Description:
Lecture 4 Prof. Dr. Ernst-Rüdiger Olderog
Christopher Bischopink, M. Sc.
  • Master
2.01.331 Intelligent and Connected Transportation Dates on Thursday. 20.10.22 - Friday. 21.10.22, Thursday. 17.11.22 - Friday. 18.11.22 10:15 - 15:45, Friday. 02.12.22 13:15 - 15:45, Thursday. 12.01.23 - Friday. 13.01.23, Wednesday. 22.02.23 - Friday. 24.02.23 10:15 - 15:45
Description:
Lecture - Prof. Dr. Frank Köster
Gerald Sauter
Dr.-Ing. Nils Müllner
  • Master
2.01.378 Practical multimodal-multisensor data analysis pipelines Monday: 10:00 - 12:00, weekly (from 17/10/22)

Description:
We know that multimodal-multisensor data is profoundly different from past data sources. It is extremely rich and dense data that typically involves multiple time-synchronized data streams, and it also can be analyzed at multiple levels such as signal, activity pattern, representational, transactional, etc. When multimodal-multisensor data are analysed at multiple levels, they constitute a vast multi-dimensional space for discovering important new phenomena with applied artificial intelligence methods (The Handbook of Multimodal-Multisensor Interfaces, Vol I, https://dl.acm.org/doi/book/10.1145/3015783). This year's course focusses on Data Analysis Pipelines for Multivariate Time Series for Sustainability: Yearly greenhouse gas emissions of OECD countries, fluctuations on the population size of endangered species, sensor readings on a biochemical reactor: multivariate time series data are generated whenever someone monitors a phenomenon over time. Extracting knowledge from them is a process that starts with obtaining the data, iteratively visualising and transforming, and finally summarising the data into an interpretable representation – whether graphical or mathematical. This course will cover good practices and practical aspects of all steps in the process – handling file input, organising a project’s code, transforming the data with spectral and machine learning methods, and generating models and visualisations that capture relevant structure in the data. Contact: Thiago S. Gouvêa thiago.gouvea@uol.de We know that multimodal-multisensor data is profoundly different from past data sources. It is extremely rich and dense data that typically involves multiple time-synchronized data streams, and it also can be analyzed at multiple levels such as signal, activity pattern, representational, transactional, etc. When multimodal-multisensor data are analysed at multiple levels, they constitute a vast multi-dimensional space for discovering important new phenomena with applied artificial intelligence methods (The Handbook of Multimodal-Multisensor Interfaces, Vol I, https://dl.acm.org/doi/book/10.1145/3015783). This year's course focusses on Data Analysis Pipelines for Multivariate Time Series for Sustainability: Yearly greenhouse gas emissions of OECD countries, fluctuations on the population size of endangered species, sensor readings on a biochemical reactor: multivariate time series data are generated whenever someone monitors a phenomenon over time. Extracting knowledge from them is a process that starts with obtaining the data, iteratively visualising and transforming, and finally summarising the data into an interpretable representation – whether graphical or mathematical. This course will cover good practices and practical aspects of all steps in the process – handling file input, organising a project’s code, transforming the data with spectral and machine learning methods, and generating models and visualisations that capture relevant structure in the data. Contact: Thiago S. Gouvêa thiago.gouvea@uol.de
Lecture - Prof. Dr.-Ing. Daniel Sonntag
Prof. Dr. Thiago Gouvea
  • Master
2.01.604 Business Intelligence I Tuesday: 14:15 - 15:45, weekly (from 18/10/22), VL
Friday: 08:15 - 09:45, weekly (from 21/10/22), Ü

Description:
Lecture 4 Viktor Dmitriyev
Dr.-Ing. Andreas Solsbach
  • Bachelor
  • Master of Education
  • Master
2.01.131 Virtual and Augmented Reality Thursday: 10:15 - 11:45, weekly (from 20/10/22), V, Location: V03 0-E002
Thursday: 14:15 - 15:45, weekly (from 20/10/22), Ü, Location: A06 5-531

Description:
Lecture 4 Prof. Dr. techn. Susanne Boll-Westermann
M. Sc. Mikolaj Wozniak
M. Sc. Tobias Lunte
  • Master
2.01.812-A Current Topics in Label-Efficient Machine Learning Wednesday: 14:15 - 15:45, weekly (from 19/10/22)

Description:
This seminar will cover current approaches to improve the label-efficiency of machine learning systems in different application domains such as computer vision, speech and natural language processing. A particular focus will lie on self-supervised learning but we will also cover aspects of self-training and weak supervision. This seminar will cover current approaches to improve the label-efficiency of machine learning systems in different application domains such as computer vision, speech and natural language processing. A particular focus will lie on self-supervised learning but we will also cover aspects of self-training and weak supervision.
Seminar 2 Prof. Dr. Nils Strodthoff
  • Master
2.01.484 Verification of Neural Networks Thursday: 14:15 - 15:45, weekly (from 20/10/22)
Thursday: 16:15 - 17:45, weekly (from 20/10/22)

Description:
Lecture 4 Prof. Dr. Daniel Neider
  • Bachelor
  • Master
2.01.1017 Oberseminar Sicherheit und Erklärbarkeit Lernender Systeme The course times are not decided yet.
Description:
Seminar - Prof. Dr. Daniel Neider
  • Bachelor
  • Master of Education
  • Master
17 Seminars

Top