Department of Computing Science

English course offer for exchange students :
School of Computing Science, Business Administration, Economics and Law
Department of Computing Science

Winter semester 2023/2024 38 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
2.01.5406b	Applied Deep Learning in PyTorch (Ü) Friday: 10:00 - 12:00, weekly (from 20/10/23) Description:	Exercises	-	Prof. Dr. Nils Strodthoff Zahra Mansour	Master
2.01.972	Fundamental Competences in Psychology III: Experiments and Studies Wednesday: 08:00 - 10:00, weekly (from 18/10/23), Location: V03 0-E003 Thursday: 12:00 - 14:00, weekly (from 19/10/23), Location: V03 2-A215 Description:	Lecture	4	Dr. Gözel Shakeri Mikolaj Wozniak Prof. Dr. Susanne Boll-Westermann	Master
2.01.5406	Applied Deep Learning in PyTorch Monday: 16:00 - 18:00, weekly (from 16/10/23) Description: This lecture provides a comprehensive introduction to contemporary Deep Learning methods, with a specific emphasis on their practical application. Concurrently, it serves as a primer for the widely-used PyTorch Deep Learning framework, assuming only a basic familiarity with Python. The course encompasses a wide range of prevalent machine learning tasks across various data types, including tabular, image, text, audio, and graph data. Throughout the course, we delve into the most crucial and up-to-date model architectures within these domains. This encompasses convolutional neural networks, recurrent neural networks, and transformer models. The lecture is complemented by hands-on exercise sessions, where students will gain practical proficiency with PyTorch. Simultaneously, they will acquire practical insights to effectively apply contemporary deep learning methods within their specific fields of interest. This lecture provides a comprehensive introduction to contemporary Deep Learning methods, with a specific emphasis on their practical application. Concurrently, it serves as a primer for the widely-used PyTorch Deep Learning framework, assuming only a basic familiarity with Python. The course encompasses a wide range of prevalent machine learning tasks across various data types, including tabular, image, text, audio, and graph data. Throughout the course, we delve into the most crucial and up-to-date model architectures within these domains. This encompasses convolutional neural networks, recurrent neural networks, and transformer models. The lecture is complemented by hands-on exercise sessions, where students will gain practical proficiency with PyTorch. Simultaneously, they will acquire practical insights to effectively apply contemporary deep learning methods within their specific fields of interest.	Lecture	2	Prof. Dr. Nils Strodthoff Juan Lopez Alcaraz Tiezhi Wang Zahra Mansour	Master
2.01.5400	Deep Unsupervised Learning Thursday: 08:00 - 10:00, weekly (from 19/10/23) Friday: 14:00 - 16:00, weekly (from 20/10/23) Description: This lecture encompasses two primary subjects: modern generative models and self-supervised learning. In the segment focusing on generative models, we will delve into a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and diffusion models. In the section dedicated to self-supervised learning, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. Additionally, we will explore pivotal papers that represent both approaches across various application domains. Concluding the lecture, we will delve into the realm of large language models and explore their diverse applications. A tutorial session will accompany the lecture, during which we will endeavor to train models using limited datasets and/or adapt pre-existing models to specific applications. This course is geared towards an advanced audience and assumes a solid foundational understanding of machine learning. Proficiency in training deep learning models is essential, preferably utilizing the PyTorch machine learning framework. This lecture encompasses two primary subjects: modern generative models and self-supervised learning. In the segment focusing on generative models, we will delve into a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and diffusion models. In the section dedicated to self-supervised learning, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. Additionally, we will explore pivotal papers that represent both approaches across various application domains. Concluding the lecture, we will delve into the realm of large language models and explore their diverse applications. A tutorial session will accompany the lecture, during which we will endeavor to train models using limited datasets and/or adapt pre-existing models to specific applications. This course is geared towards an advanced audience and assumes a solid foundational understanding of machine learning. Proficiency in training deep learning models is essential, preferably utilizing the PyTorch machine learning framework.	Lecture	4	Prof. Dr. Nils Strodthoff Juan Lopez Alcaraz Tiezhi Wang	Master
2.01.5100	Digital Technology on Energy Markets Tuesday: 14:00 - 16:00, weekly (from 17/10/23), Location: V04 1-146 Wednesday: 14:00 - 16:00, weekly (from 18/10/23), Location: V03 0-E003, V03 0-C003 Description: - Modelling of power markets - Evaluation of design alternatives on power markets - Optimization of power market systems - Describing and evaluating operation strategies on power markets - Modelling of power markets - Evaluation of design alternatives on power markets - Optimization of power market systems - Describing and evaluating operation strategies on power markets	Lecture	4	Prof. Dr. Philipp Staudt	Master
2.01.368	Selected Topics in Microwave-Microscopy and -Communication Systems Wednesday: 14:00 - 16:00, weekly (from 18/10/23) Description:	Seminar	2	Dr. Muhammad Yasir Anja Hiller	Master
2.01.535	Evolution Strategies The course times are not decided yet. Description: This course gives an introduction to optimization. It introduces a (1+1)-ES, population-based evolution strategies, parameter control, self-adaptation, and leads to the famous CMA-ES. Besides introducing the foundations of natural evolution strategies, restart strategies, constraint handling, and multi-objective optimization is introduced. This course gives an introduction to optimization. It introduces a (1+1)-ES, population-based evolution strategies, parameter control, self-adaptation, and leads to the famous CMA-ES. Besides introducing the foundations of natural evolution strategies, restart strategies, constraint handling, and multi-objective optimization is introduced.	Lecture	-	Prof. Dr. Oliver Kramer	Master
2.01.5406a	Applied Deep Learning in PyTorch (Ü) Wednesday: 16:00 - 18:00, weekly (from 18/10/23) Description:	Exercises	2	Prof. Dr. Nils Strodthoff Tiezhi Wang	Master
2.01.1210	Practical multimodal-multisensor data analysis pipelines Monday: 10:15 - 11:45, weekly (from 16/10/23) Thursday: 10:15 - 11:45, weekly (from 19/10/23), Übung 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	2	Thiago Gouvea Prof. Dr. Daniel Sonntag Ilira Troshani	Master
2.01.369	Selected topics in nanomechanics and the mechanical characterization of nanomaterials using microscopy Tuesday: 14:00 - 16:00, weekly (from 17/10/23) Description:	Seminar	2	Dr. James Mead Anja Hiller	Master
2.01.490	Seminar: Logical Methods in AI Verification Wednesday: 08:00 - 10:00, weekly (from 18/10/23) Description:	Seminar	2	Prof. Dr. Heike Wehrheim	Master
2.01.977	Fundamental Competences in Psychology II: Experimental Psychology and Cognitive Processes Thursday: 08:00 - 10:00, weekly (from 19/10/23) Description:	Seminar	2	Marcel Saager, M. Sc.	Master
2.01.489	Verification of Parallel Programs Tuesday: 14:00 - 16:00, weekly (from 17/10/23), Location: A05 1-160 Wednesday: 08:00 - 10:00, weekly (from 18/10/23), Location: A10 1-121 (Hörsaal F) Description:	Lecture	4	Prof. Dr. Heike Wehrheim Lara Bargmann	Master
2.01.5452	Current Topics in Interpretable/Explainable AI (XAI) Monday: 08:00 - 10:00, weekly (from 16/10/23) 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
2.01.5460	Applied AI - Multimodal-Multisensor Interfaces 3: Language Processing, Software, Commercialization, and Emerging Directions The course times are not decided yet. Description: This third seminar takes the contents of the first two seminars—namely, the motivations, foundational concepts, basic modality combinations, component analyses, and recognition and fusion techniques—to the next level. MMI 3 discusses how to design and build functioning multimodal-multisensor systems that can sustain real-world use. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. It is very useful if you want to work on practical applications, transfer of AI technology to application domains such as medicine and healthcare, and industrial prototypes. Afterward, students might engage in a hands-on project in which they design, build, and evaluate the performance of a multimodal system in our project group MMI II (https://elearning.uni-oldenburg.de/dispatch.php/course/details?sem_id=098bd500a63e723551364c7f921755b5&again=yes). Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging Directions - Volume 3" (https://dl.acm.org/doi/book/10.1145/3233795). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de This third seminar takes the contents of the first two seminars—namely, the motivations, foundational concepts, basic modality combinations, component analyses, and recognition and fusion techniques—to the next level. MMI 3 discusses how to design and build functioning multimodal-multisensor systems that can sustain real-world use. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. It is very useful if you want to work on practical applications, transfer of AI technology to application domains such as medicine and healthcare, and industrial prototypes. Afterward, students might engage in a hands-on project in which they design, build, and evaluate the performance of a multimodal system in our project group MMI II (https://elearning.uni-oldenburg.de/dispatch.php/course/details?sem_id=098bd500a63e723551364c7f921755b5&again=yes). Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging Directions - Volume 3" (https://dl.acm.org/doi/book/10.1145/3233795). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de	Seminar	-	Hannes Kath Prof. Dr. Daniel Sonntag Ilira Troshani	Master
2.01.961	Fundamental Competences in Computing Science II: Mathematics Tuesday: 14:00 - 16:00, weekly (from 17/10/23) Tuesday: 16:00 - 18:00, weekly (from 17/10/23) Description:	Lecture	4	Dr. Sandra Stein	Master
2.01.131	VRAR Lecture Thursday: 10:00 - 12:00, weekly (from 19/10/23) Thursday: 14:00 - 16:00, weekly (from 19/10/23) Description:	Lecture	2	Mikolaj Wozniak Prof. Dr. Susanne Boll-Westermann Dr. Gözel Shakeri Tobias Lunte Wilko Heuten	Master
2.01.108	Requirements Engineering and Management Wednesday: 12:00 - 14:00, weekly (from 18/10/23) Thursday: 12:00 - 14:00, weekly (from 19/10/23) Description: The basic terms and concepts of requirements analysis are taught, and methods and techniques of requirements elicitation and management are discussed and practically tested. Topics covered include: -Need for requirements elicitation and requirements management. -requirements engineering in the software development process (in the waterfall model, in the unified process, in agile development) -Requirements engineering process (participants, documents, activities) -Understand application domain (create vision, document system environment, create domain model, identify use cases) -Evoke requirements (functional and non-functional requirements, gather requirements, document requirements, validate requirements, negotiate requirements) -Manage requirements The basic terms and concepts of requirements analysis are taught, and methods and techniques of requirements elicitation and management are discussed and practically tested. Topics covered include: -Need for requirements elicitation and requirements management. -requirements engineering in the software development process (in the waterfall model, in the unified process, in agile development) -Requirements engineering process (participants, documents, activities) -Understand application domain (create vision, document system environment, create domain model, identify use cases) -Evoke requirements (functional and non-functional requirements, gather requirements, document requirements, validate requirements, negotiate requirements) -Manage requirements	Lecture	4	Prof. Dr. Andreas Winter	Master
2.01.586	Smart Grid Research Thursday: 10:00 - 12:00, weekly (from 19/10/23) Description:	Seminar	-	Prof. Dr. Sebastian Lehnhoff Prof. Dr. Astrid Nieße Julia Catharina Heiken	Bachelor Master of Education Master
2.01.341	Robust Control and State Estimation in Digitalised Energy Systems Monday: 16:00 - 18:00, weekly (from 16/10/23), Location: A14 1-115, V03 0-D001 Tuesday: 16:00 - 18:00, weekly (from 17/10/23), Location: A14 1-115, V03 0-D001 Description: 1. Robustness of linear systems/ system analysis • Boundary crossing theorem of Frazer and Duncan • Mikhailov criterion • Kharitonov criterion • Frequency response approaches 2. Selected control design techniques/ control synthesis • Parameter-space approach of Ackermann and Kaesbauer • Eigenvalue and eigenvalue domain assignment • H-infinity control • Frequency response approaches (Sensitivity function approaches in the frequency domain) 3. Robust LMI-based control techniques • Lyapunov stability • Polytopic uncertainty modeling • Optimality of solutions 4. Duality between control and observer synthesis • Robust state estimation • Sliding mode observers 5. Interval methods: Solution of static and dynamic problems (Enclosing function values, Branch-and-bound techniques, Verification techniques for differential equations) 6. Fundamentals: Fault detection and fault-tolerant control 1. Robustness of linear systems/ system analysis • Boundary crossing theorem of Frazer and Duncan • Mikhailov criterion • Kharitonov criterion • Frequency response approaches 2. Selected control design techniques/ control synthesis • Parameter-space approach of Ackermann and Kaesbauer • Eigenvalue and eigenvalue domain assignment • H-infinity control • Frequency response approaches (Sensitivity function approaches in the frequency domain) 3. Robust LMI-based control techniques • Lyapunov stability • Polytopic uncertainty modeling • Optimality of solutions 4. Duality between control and observer synthesis • Robust state estimation • Sliding mode observers 5. Interval methods: Solution of static and dynamic problems (Enclosing function values, Branch-and-bound techniques, Verification techniques for differential equations) 6. Fundamentals: Fault detection and fault-tolerant control	Lecture	4	Prof. Dr. Andreas Rauh Marit Lahme Oussama Benzinane	Master
2.01.604	Business Intelligence (Ü) Friday: 08:00 - 10:00, weekly (from 20/10/23) Description:	Exercises	-	Viktor Dmitriyev Dr.-Ing. Andreas Solsbach Barbara Bremer-Rapp	Bachelor Master of Education Master
2.01.5456	Applied AI - Multimodal-Multisensor Interfaces 1: Foundations, User Modeling, and Common Modality Combination The course times are not decided yet. Description: We look at relevant theory and neuroscience foundations for guiding the development of high-performance systems. We discuss approaches to user modeling, interface design that supports user choice, synergistic combination of modalities with sensors, and blending of multimodal input and output. We also highlight an in-depth look at the most common multimodal-multisensor combinations- for example, touch and pen input, haptic and non-speech audio output, and speech co-processed with visible lip movements, gaze, gestures, or pen input. A common theme throughout is support for mobility and individual differences among users-including the world's rapidly growing population of seniors. This seminar would be most appropriate for graduate students, and of primary interest to students studying computer science and information technology, human–computer interfaces, mobile and ubiquitous interfaces, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 1" (https://dl.acm.org/doi/book/10.1145/3015783). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Ilira Troshani, ilira.troshani@uni-oldenburg.de We look at relevant theory and neuroscience foundations for guiding the development of high-performance systems. We discuss approaches to user modeling, interface design that supports user choice, synergistic combination of modalities with sensors, and blending of multimodal input and output. We also highlight an in-depth look at the most common multimodal-multisensor combinations- for example, touch and pen input, haptic and non-speech audio output, and speech co-processed with visible lip movements, gaze, gestures, or pen input. A common theme throughout is support for mobility and individual differences among users-including the world's rapidly growing population of seniors. This seminar would be most appropriate for graduate students, and of primary interest to students studying computer science and information technology, human–computer interfaces, mobile and ubiquitous interfaces, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 1" (https://dl.acm.org/doi/book/10.1145/3015783). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Ilira Troshani, ilira.troshani@uni-oldenburg.de	Seminar	-	Hannes Kath Prof. Dr. Daniel Sonntag Ilira Troshani	Master
2.01.604	Business Intelligence (V) Tuesday: 14:00 - 16:00, weekly (from 17/10/23) Description:	Lecture	2	Dr.-Ing. Andreas Solsbach Viktor Dmitriyev Barbara Bremer-Rapp	Bachelor Master of Education Master
2.01.514	Simulation-based Smart Grid Engineering and Assessment Tuesday: 10:00 - 12:00, weekly (from 17/10/23), Location: V03 3-A309 Thursday: 12:00 - 14:00, weekly (from 19/10/23), Location: V03 2-A208 Description:	Lecture	4	Jörg Bremer	Master
2.01.5126	Digitalised Energy System Cyber-Resilience Wednesday: 10:00 - 12:00, fortnightly (from 18/10/23) Description:	Seminar	2	Prof. Dr. Sebastian Lehnhoff Jörg Bremer	Master
2.01.813	Grundlagen der Kausaltheorie Monday: 14:00 - 16:00, weekly (from 16/10/23) Description: Lerngegenstand des Seminars sind die Grundlagen der Kausaltheorie nach Judea Pearl, welches das Konzept des randomisierten Kontrollexperiments zur Bestimmung kausaler Effekte verallgemeinert und formalisiert. Hierbei werden insbesondere gerichtete, azyklische Graphen verwendet, um die Annahmen der kausalen Beziehungen zwischen Zufallsvariablen abzubilden und, basierend darauf, algorithmisch Störvariablen zu identifizieren. Insbesondere wird hierbei auch die Bestimmung kausaler Effekte auf Grundlage von Beobachtungsdaten ermöglicht. Lerngegenstand des Seminars sind die Grundlagen der Kausaltheorie nach Judea Pearl, welches das Konzept des randomisierten Kontrollexperiments zur Bestimmung kausaler Effekte verallgemeinert und formalisiert. Hierbei werden insbesondere gerichtete, azyklische Graphen verwendet, um die Annahmen der kausalen Beziehungen zwischen Zufallsvariablen abzubilden und, basierend darauf, algorithmisch Störvariablen zu identifizieren. Insbesondere wird hierbei auch die Bestimmung kausaler Effekte auf Grundlage von Beobachtungsdaten ermöglicht.	Seminar	2	Prof. Dr. Martin Georg Fränzle Christian Neurohr Lina Putze Tjark Koopmann	Master
2.01.5454	Advanced Topics in Medical Data Analysis with Deep Learning Monday: 12:00 - 14:00, weekly (from 16/10/23) Description:	Seminar	2	Prof. Dr. Nils Strodthoff Juan Lopez Alcaraz	Master
2.01.5458	Applied AI - Multimodal-Multisensor Interfaces 2: Signal Processing, Architectures, and Detection of Emotion and Cognition The course times are not decided yet. Description: We begin with multimodal signal processing, architectures, and machine learning. It includes recent deep-learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. We discuss real-time multimodal analysis of emotion and social signals from various modalities and perception of affective expression by users. Then we discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology, and tutorial support for mastering this rapidly expanding field. Finally, we look at experts' exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 2" (https://dl.acm.org/doi/book/10.1145/3107990). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de We begin with multimodal signal processing, architectures, and machine learning. It includes recent deep-learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. We discuss real-time multimodal analysis of emotion and social signals from various modalities and perception of affective expression by users. Then we discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology, and tutorial support for mastering this rapidly expanding field. Finally, we look at experts' exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 2" (https://dl.acm.org/doi/book/10.1145/3107990). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de	Seminar	-	Hannes Kath Prof. Dr. Daniel Sonntag Ilira Troshani	Master
2.01.017	Interactive Systems Monday: 10:00 - 12:00, weekly (from 16/10/23), Location: S 2-204 Friday: 10:00 - 12:00, weekly (from 20/10/23), Location: V03 0-M018 Description:	Lecture	4	Prof. Dr. Susanne Boll-Westermann Tobias Lunte Dr. Gözel Shakeri Mikolaj Wozniak	Bachelor Master of Education
2.01.964	Foundations of STS Eg.: Psychology and Philosophy of Technology Friday: 10:00 - 12:00, weekly (from 20/10/23) Description:	Lecture	2	Prof. Dr. Mark Schweda Eike Buhr	Master
2.01.5114	Digitalised Energy System Requirements Engineering Tuesday: 12:00 - 14:00, weekly (from 17/10/23), Location: A01 0-006 Friday: 12:00 - 14:00, weekly (from 20/10/23), Location: A04 2-221 Description:	Lecture	2	Prof. Dr. Sebastian Lehnhoff Jörg Bremer	Master
2.01.174	Wearable Computing Monday: 14:00 - 16:00, weekly (from 16/10/23) Monday: 16:00 - 18:00, weekly (from 16/10/23) Description:	Project	-	Tobias Lunte Prof. Dr. Susanne Boll-Westermann Dr. Gözel Shakeri Mikolaj Wozniak	Master
2.01.516	Distributed Operation in Digitalised Energy Systems Wednesday: 08:00 - 10:00, weekly (from 18/10/23), Location: A05 1-160 Thursday: 16:00 - 18:00, weekly (from 19/10/23), Location: V04 0-033 Description: Ehemals Agentenbasierte Verfahren in der Energieversorgung Ehemals Agentenbasierte Verfahren in der Energieversorgung	Lecture	4	Prof. Dr. Astrid Nieße Jens Sager	Master
2.01.5130	Socio-technical Energy Systems Wednesday: 10:00 - 12:00, fortnightly (from 25/10/23) Description:	Seminar	2	Prof. Dr. Sebastian Lehnhoff Jörg Bremer	Master
2.01.980	CS4Science Tuesday: 08:00 - 10:00, weekly (from 17/10/23), V, Location: A01 0-006 Thursday: 16:00 - 18:00, weekly (from 19/10/23), V+Ü, Location: A01 0-008 Description: This cource introduces basic concepts of Computer Science and also a short introduction to programming in the programming language Python. This cource introduces basic concepts of Computer Science and also a short introduction to programming in the programming language Python.	Lecture	4	Dr. Ute Vogel-Sonnenschein	Bachelor Master
2.01.5102	Power System Components, Networks, Operation Monday: 10:00 - 12:00, weekly (from 16/10/23), V, Location: V03 0-D003, V03 0-D001 Monday: 12:00 - 14:00, weekly (from 16/10/23), Ü, Location: V03 0-D003, V03 0-D001 Description:	Lecture	4	Wolfgang Gawlik	Master
2.01.966	Foundations of STS Eng.: Statistics and Programming Tuesday: 12:00 - 14:00, weekly (from 17/10/23) Thursday: 10:00 - 12:00, weekly (from 19/10/23) Description:	Lecture	4	Dr. Fabian Otto-Sobotka	Master
2.01.340	Uncertainty Modelling for Control in Digitalised Energy Systems Thursday: 08:00 - 10:00, weekly (from 19/10/23) Friday: 08:00 - 10:00, weekly (from 20/10/23) Description: 1. Mathematical modeling of uncertainty in linear and nonlinear dynamic systems 2. Stochastic modeling approaches • Probability distributions • Bayesian state estimation for discrete-time systems (linear/nonlinear) and for continuous-time systems (linear) • Linear estimation techniques in an extended state-space (Carleman linearization for special system classes) • Monte-Carlo methods 3. Estimation of states, parameters and simulation of uncertain processes • Outlook: Markov models • Outlook: Bayesian networks 4. Set-based approaches • Set-based algorithms: Forward-backward contractor and bisection techniques • Interval methods for a verified solution of ordinary differential equations and for a stability proof of uncertain systems • Estimation of states and parameters as well as simulation of uncertain processes 5. Outlook: Synthesis of controllers and state observers under an explicit description of uncertainty 1. Mathematical modeling of uncertainty in linear and nonlinear dynamic systems 2. Stochastic modeling approaches • Probability distributions • Bayesian state estimation for discrete-time systems (linear/nonlinear) and for continuous-time systems (linear) • Linear estimation techniques in an extended state-space (Carleman linearization for special system classes) • Monte-Carlo methods 3. Estimation of states, parameters and simulation of uncertain processes • Outlook: Markov models • Outlook: Bayesian networks 4. Set-based approaches • Set-based algorithms: Forward-backward contractor and bisection techniques • Interval methods for a verified solution of ordinary differential equations and for a stability proof of uncertain systems • Estimation of states and parameters as well as simulation of uncertain processes 5. Outlook: Synthesis of controllers and state observers under an explicit description of uncertainty	Lecture	4	Prof. Dr. Andreas Rauh Marit Lahme Oussama Benzinane	Master
38 Seminars

Top