Stud.IP Uni Oldenburg
University of Oldenburg
02.12.2022 04:08:48
Veranstaltungsverzeichnis

Department of Computing Science Click here for PDF-Download

Winter semester 2022/2023 7 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 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
2.01.812-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
M. Sc. Juan Lopez Alcaraz
  • Master
2.01.813-D Selected Topics in IT-Security Thursday: 16:15 - 17:45, weekly (from 20/10/22)

Description:
/// Goals of the course /// At the end of the course, students will be able to - analyze the technical merits of specific developments within the field of IT-security, - substantiate their analyses using existing and scientific documented knowledge, - clearly write up those analyses in a concise scientific report, and - further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic in the field of IT-security. There will be multiple topics, and each topic is to be tackled by an individual student; individually supervised by the teaching staff. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic and will analyze it according to topic-specific criteria (to be defined in consultation with the individual supervisor). Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 30 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (40%), their presentation and consequent discussion (40%), and the general process (20%; includes: independence, planning, active involvement, …) /// Possible topics (incomplete; final list will be presented in the first lecture) /// More details and a first academic reference on each topic can be found on this course's Stud.IP page under "Schedule (Ablaufplan)" --> "Topics (Themen)". - (Semi-)Automated Security Event Handling in Security Operations Centers - Attacks on Searchable Encrypted Databases and Countermeasures - Automated Extraction of Tactics, Techniques, and Procedures from Cyber Threat Reports - Explainable Machine Learning in Security - Zero-Knowledge Proofs - Mobile-App Fingerprinting on Encrypted Network Traffic - The Right to be Forgotten: Machine Unlearning and its Implications - Privacy-Preserving Wi-Fi-based Crowd Monitoring - Your Own Topic /// Goals of the course /// At the end of the course, students will be able to - analyze the technical merits of specific developments within the field of IT-security, - substantiate their analyses using existing and scientific documented knowledge, - clearly write up those analyses in a concise scientific report, and - further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic in the field of IT-security. There will be multiple topics, and each topic is to be tackled by an individual student; individually supervised by the teaching staff. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic and will analyze it according to topic-specific criteria (to be defined in consultation with the individual supervisor). Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 30 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (40%), their presentation and consequent discussion (40%), and the general process (20%; includes: independence, planning, active involvement, …) /// Possible topics (incomplete; final list will be presented in the first lecture) /// More details and a first academic reference on each topic can be found on this course's Stud.IP page under "Schedule (Ablaufplan)" --> "Topics (Themen)". - (Semi-)Automated Security Event Handling in Security Operations Centers - Attacks on Searchable Encrypted Databases and Countermeasures - Automated Extraction of Tactics, Techniques, and Procedures from Cyber Threat Reports - Explainable Machine Learning in Security - Zero-Knowledge Proofs - Mobile-App Fingerprinting on Encrypted Network Traffic - The Right to be Forgotten: Machine Unlearning and its Implications - Privacy-Preserving Wi-Fi-based Crowd Monitoring - Your Own Topic
Seminar 2 Prof. Dr. Andreas Peter
Valentin Reyes Häusler
  • Master
2.01.963 Fundamentals of Psychology: Cognition Thursday: 08:15 - 09:45, weekly (from 27/10/22)
Dates on Thursday. 27.10.22 08:15 - 09:45

Description:
Seminar - Prof. Dr. habil. Christoph Siegfried Herrmann, Dipl.-Ing.
María Paula Villabona Orozco
  • Master
2.01.813-E 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
7 Seminars

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