• • Dieser Kurs wird auf Englisch angeboten/ this course is held in English.

Im Sommersemester steht mit PMWK eine Alternative auf Deutsch zur Verfügung --

We discuss different aspects of science, academia and academic writing. At the same time, the course aims to help you understand your own role as a master student and job related goals. The course is both a preparation for studying at a master level and subsequent work in different roles in academia, business, the public sector or as an entrepreneur in Computer Science and Artificial Intelligence. Therefore, it is useful to take this course towards the beginning of your studies.

Students with the Subject "Master in Computer Science" can also complete the module "Projektmanagement und Wissenschaftskultur" with this course.

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

https://lvb.informatik.uni-bremen.de/imap/03-imap-aswe.pdf

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec1.pdf
Entertainment Computing I beschäftigt sich mit theoretischen Grundlagen, psychologischen Aspekten und methodischen Verfahren rund um digitale Spiele und interaktive Unterhaltungssysteme. Im Mittelpunkt stehen die Analyse und das Verständnis von Spielkonzepten, Nutzerverhalten und Evaluationsmethoden.

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec1.pdf
Entertainment Computing I beschäftigt sich mit theoretischen Grundlagen, psychologischen Aspekten und methodischen Verfahren rund um digitale Spiele und interaktive Unterhaltungssysteme. Im Mittelpunkt stehen die Analyse und das Verständnis von Spielkonzepten, Nutzerverhalten und Evaluationsmethoden.

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-air.pdf
Die Veranstaltung findet im DFKI statt.

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Profil: KIKR
Schwerpunkt: IMVP-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-bcod.pdf

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-gme.pdf
Termine: 10-tägiger Blockkurs nach der Vorlesungszeit.

Wird als hybrider Blockkurs nach der Vorlesungszeit angeboten. Bei Interesse frühzeitig bei felix.putzeprotect me ?!uni-bremenprotect me ?!.de anmelden, da begrenzte Teilnehmer:innen-Zahl. Eine Anmeldung nur über StudIP ist nicht ausreichend.

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: KIKR
Schwerpunkt: IMVP- AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mlar.pdf
Die Veranstaltung findet in den Räumen des DFKI statt.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR
Schwerpunkt: IMVP-VMC, IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-sprs.pdf
Die Vorlesung findet online per Zoom statt.

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-tcrs.pdf
Schwerpunkt: IMVP-AI

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-air.pdf
Die Veranstaltung findet im DFKI statt.

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Profil: KIKR
Schwerpunkt: IMVP-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-bcod.pdf

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

Schwerpunkt: IMVA-DMI, IMVA-AI
Abstract: The shift towards human-like interaction with computers through voice and text has made large language models like ChatGPT and Gemini a part of everyday life. While these open-domain chatbots are powerful, their "one-size-fits-all" approach often leads to user frustrations, including the need for overly specific prompts, irrelevant or verbose responses, and a lack of critical context.
Often, domain-specific interfaces are important for improving user experience. This offers the opportunity to build specific user interfaces or manipulate the language depending on the domain. We will explore how adapting natural language interfaces to specific fields (e.g., medical advice or creative writing) can resolve these common issues. In the seminar, students will investigate a specific research question, analyze relevant academic literature, and then design, implement, and evaluate a novel domain-specific user interface. The goal is to move beyond generic chatbots and create more intelligent, context-aware, and effective natural language tools.

We will work very hands-on and will have many sessions where we implement something. For the first session bring a laptop. You should have a GitLab account and Python (version 3.10 or later) installed on your computer. The course combines research with development, and each student has to give a presentation in the course and develop a prototype.

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-gme.pdf
Termine: 10-tägiger Blockkurs nach der Vorlesungszeit.

Wird als hybrider Blockkurs nach der Vorlesungszeit angeboten. Bei Interesse frühzeitig bei felix.putzeprotect me ?!uni-bremenprotect me ?!.de anmelden, da begrenzte Teilnehmer:innen-Zahl. Eine Anmeldung nur über StudIP ist nicht ausreichend.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gpmlr.pdf

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: KIKR
Schwerpunkt: IMVP- AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mlar.pdf
Die Veranstaltung findet in den Räumen des DFKI statt.

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: KIKR
Schwerpunkt: IMVP-VMC, IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-sprs.pdf
Die Vorlesung findet online per Zoom statt.

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

https://lvb.informatik.uni-bremen.de/imva/03-imva-tmp.pdf
Schwerpunkt: IMVA-DMI
https://bernardrobben.net/wp-content/uploads/2023/03/TheMachinicProject.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-tcrs.pdf
Schwerpunkt: IMVP-AI

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

https://lvb.informatik.uni-bremen.de/ims/03-ims-allm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Prior knowledge:
Basic knowledge of artificial intelligence (AI) and machine learning (ML), as acquired from
one of the introductory and foundational machine learning, AI, or cognitive systems lectures.
Advanced course knowledge, e.g. as acquired in "Life-long Machine Learning" or "Advanced
Machine Learning", is not required, but will be beneficial to engage with more recent in-depth
perspectives at the frontier of current research.

Learning Outcomes:
Traditional machine learning studies the design of models and training algorithms to learn to
solve tasks from data. In contrast to humans, who excel at adaptivity and benefit greatly from
learning curricula, machine learners are largely tailored to stationary data and static test
scenarios. Throughout the seminar, students will engage with state-of-the-art literature that
advances the frontiers of lifelong learning machines. They will improve their ability to
navigate scientific works, critically assess contributions and prospects, inspect the newest
algorithms, and identify persisting limitations and open research questions. In the process,
students will refine their scientific presentation skills and strengthen their competence in
participating in advanced machine learning discourse.

Content:
The seminar will cover different elements required to transform static machine learners into
lifelong learning machines. At the start of the seminar, students will get to select from a
choice of topics spanning a variety of modern perspectives and state-of-the-art lifelong
learning algorithms. In prospective in-depth engagement with the chosen literature as the
starting point, presentations will be prepared and held in front of the seminar participants to
fuel weekly scientific discourse. Discussed advances towards lifelong learning machines
encompass, but are not limited to:
* Memory: what consistutes memorable data points and episodes? How can we construct
concrete algorithms to identify and extract memorable experience? What types of
memories do humans possess and how do machine learning algorithms acommodate
them?
* Learning curricula: how can we identify suitable learning curricula for machine learning?
Which modern algorithms exist to enable models to benefit from structured curricula and
data streams?
* Continual optimization: how can we transcend the greediness of machine learning
optimizers? What algorithmic advances exist to effectively regularize past parameters,
representations, and knowledge?
* Adaptive and modular architectures: how can we dynamically modify opaque
architectures, such as deep neural networks? Which techniques exist to modularize
functions and disambiguate entangled representations?
* Lifelong ML theory: what are the advances in transferring machine learning theory to
lifelong machine learning? What are the insights and obstacles learned from grounded
advances analyzable, linear systems?
* Evaluation: How do we evaluate dimensions with inherent trade-offs, e.g. the pursuit of
stability and plasticity? How do we select hyperparameters in scenarios that are
constantly evolving? What techniques exist to enable fair comparisons across a
plethora of metrics of interest?

https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf

Das Seminar findet im Mevis Gebäude, Raum 0.10.C, statt.
https://lvb.informatik.uni-bremen.de/ims/03-ims-dlmb.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-shar.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-iuag.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 14.10.2025 um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

In this seminar, we learn the basics of bandit problems and algorithms, a framework for sequential decision tasks with exploration-exploitation tradeoff. We will see variations, extensions, and theoretical guarantees for the k-armed bandit problems and its applications in, e.g., reinforcement learning.

https://lvb.informatik.uni-bremen.de/ims/03-ims-srse.pdf

Nach der MPO 2020 ist diese LV ein Masterseminar, keine Master-Aufbau Veranstaltung.
https://lvb.informatik.uni-bremen.de/ims/03-ims-softc.pdf

Schwerpunkt: AI

Schwerpunkt: SQ

Schwerpunkt: AI

Schwerpunkt: SQ, AI

Schwerpunkt: SQ

Schwerpunkt:

Schwerpunkt: AI

Schwerpunkt: DMI

Schwerpunkt: VMC

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Seminararbeit und Vortrag über ausgewählte Publikation aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Schwerpunkt: IMVA-DMI, IMVA-AI
Abstract: The shift towards human-like interaction with computers through voice and text has made large language models like ChatGPT and Gemini a part of everyday life. While these open-domain chatbots are powerful, their "one-size-fits-all" approach often leads to user frustrations, including the need for overly specific prompts, irrelevant or verbose responses, and a lack of critical context.
Often, domain-specific interfaces are important for improving user experience. This offers the opportunity to build specific user interfaces or manipulate the language depending on the domain. We will explore how adapting natural language interfaces to specific fields (e.g., medical advice or creative writing) can resolve these common issues. In the seminar, students will investigate a specific research question, analyze relevant academic literature, and then design, implement, and evaluate a novel domain-specific user interface. The goal is to move beyond generic chatbots and create more intelligent, context-aware, and effective natural language tools.

We will work very hands-on and will have many sessions where we implement something. For the first session bring a laptop. You should have a GitLab account and Python (version 3.10 or later) installed on your computer. The course combines research with development, and each student has to give a presentation in the course and develop a prototype.

Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec1.pdf
Entertainment Computing I beschäftigt sich mit theoretischen Grundlagen, psychologischen Aspekten und methodischen Verfahren rund um digitale Spiele und interaktive Unterhaltungssysteme. Im Mittelpunkt stehen die Analyse und das Verständnis von Spielkonzepten, Nutzerverhalten und Evaluationsmethoden.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gpmlr.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

https://lvb.informatik.uni-bremen.de/imva/03-imva-tmp.pdf
Schwerpunkt: IMVA-DMI
https://bernardrobben.net/wp-content/uploads/2023/03/TheMachinicProject.pdf

https://lvb.informatik.uni-bremen.de/igs/03-ibfs-tstud.pdf
Zu Beginn jedes Semesters findet eine Infoveranstaltung statt, wo die Scheinkriterien für Versuchspersonen erläutert werden. Außerdem werden (aufbauend auf den Inhalten von WA1) Forschungsmethoden von Studien mit Versuchspersonen vermittelt.
Im weiteren Verlauf des Studiums sollen Studierende 15 Versuchspersonenstunden absolvieren (d.h. an mehreren Studien teilnehmen). Jede Studienteilnahme wird mit Versuchspersonenstunden vergütet, die in ECTS anerkannt werden können. Die Versuchspersonenstunden können über mehre Semester gesammelt werden.
Die Teilnahme an den Studien soll in einer schriftlichen Ausarbeitung dokumentiert und reflektiert werden.



At the beginning of each semester, an information session is held where the certificate criteria for subjects are explained. In addition, research methods of studies with test subjects are taught (building on the contents of WA1).

Later in the program, students are expected to complete 15 subject hours (i.e., participate in different studies). Each study participation is compensated with subject hours, which can be recognized in ECTS. The subject hours may be accumulated over multiple semesters.

Participation in the studies should be documented and reflected upon in a written paper at the end.
The selection of participants is made manually after registration.

Users who wish to register for this event will receive more detailed information and can then still decide against participation.

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

https://lvb.informatik.uni-bremen.de/imap/03-imap-aswe.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-allm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Prior knowledge:
Basic knowledge of artificial intelligence (AI) and machine learning (ML), as acquired from
one of the introductory and foundational machine learning, AI, or cognitive systems lectures.
Advanced course knowledge, e.g. as acquired in "Life-long Machine Learning" or "Advanced
Machine Learning", is not required, but will be beneficial to engage with more recent in-depth
perspectives at the frontier of current research.

Learning Outcomes:
Traditional machine learning studies the design of models and training algorithms to learn to
solve tasks from data. In contrast to humans, who excel at adaptivity and benefit greatly from
learning curricula, machine learners are largely tailored to stationary data and static test
scenarios. Throughout the seminar, students will engage with state-of-the-art literature that
advances the frontiers of lifelong learning machines. They will improve their ability to
navigate scientific works, critically assess contributions and prospects, inspect the newest
algorithms, and identify persisting limitations and open research questions. In the process,
students will refine their scientific presentation skills and strengthen their competence in
participating in advanced machine learning discourse.

Content:
The seminar will cover different elements required to transform static machine learners into
lifelong learning machines. At the start of the seminar, students will get to select from a
choice of topics spanning a variety of modern perspectives and state-of-the-art lifelong
learning algorithms. In prospective in-depth engagement with the chosen literature as the
starting point, presentations will be prepared and held in front of the seminar participants to
fuel weekly scientific discourse. Discussed advances towards lifelong learning machines
encompass, but are not limited to:
* Memory: what consistutes memorable data points and episodes? How can we construct
concrete algorithms to identify and extract memorable experience? What types of
memories do humans possess and how do machine learning algorithms acommodate
them?
* Learning curricula: how can we identify suitable learning curricula for machine learning?
Which modern algorithms exist to enable models to benefit from structured curricula and
data streams?
* Continual optimization: how can we transcend the greediness of machine learning
optimizers? What algorithmic advances exist to effectively regularize past parameters,
representations, and knowledge?
* Adaptive and modular architectures: how can we dynamically modify opaque
architectures, such as deep neural networks? Which techniques exist to modularize
functions and disambiguate entangled representations?
* Lifelong ML theory: what are the advances in transferring machine learning theory to
lifelong machine learning? What are the insights and obstacles learned from grounded
advances analyzable, linear systems?
* Evaluation: How do we evaluate dimensions with inherent trade-offs, e.g. the pursuit of
stability and plasticity? How do we select hyperparameters in scenarios that are
constantly evolving? What techniques exist to enable fair comparisons across a
plethora of metrics of interest?

Schwerpunkt: IMVT-AI

Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-air.pdf
Die Veranstaltung findet im DFKI statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-ata.pdf

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-bs.pdf

Profil: KIKR
Schwerpunkt: IMVP-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-bcod.pdf

Schwerpunkt: DMI, VMC
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-cg.pdf
Programmierkenntnisse sind Voraussetzung (ein erfolgreicher Abschluss des "Propädeutikums C" wird empfohlen), ebenso wie algorithmisches Denken, eine gewisse Vertrautheit mit mathematischer Begriffsbildung und Vorgehensweise.

Diese Vorlesung soll sowohl eine Einführung in die theoretischen und methodischen Grundlagen der Computergraphik geben, als auch die Grundlagen für die praktische Implementierung von computergraphischen Systemen legen. Der Schwerpunkt liegt auf Algorithmen und Konzepten zur Repräsentation und Visualisierung von polygonalen, 3-dimensionalen graphischen Szenen.
Der Inhalt umfasst in der Regel folgende Themen:
Mathematische Grundlagen; OpenGL and C ; 2D Algorithmen der Computergrafik (Scan Conversion, Visibility Computations, etc.); Theorie der Farben, Farbräume (hauptsächlich physikalische, neurologische, und technische Aspekte); 3D Computergraphik (Rendering Pipeline, Transformationen, Beleuchtung, etc.); Techniken zum Echtzeit-Rendering; Das Konzept und die Programmierung von Shadern; Texturierung (Einordnung in die Pipeline, einfache Parametrisierung, etc.).

Die Übungsaufgaben werden teils theoretisch, teils praktisch sein, wobei die praktischen Aufgaben gewisse Programmierfähigkeiten in C verlangen. (Zu Beginn der Vorlesung wird deshalb nochmals ein kurzer "Refresh" Ihrer C/C-Kenntnisse gemacht.) Die Vorlesung setzt eine gewisse mathematische, algorithmische und programmiertechnische Gewandtheit voraus, fördert diese aber auch und führt sie weiter.
https://cgvr.cs.uni-bremen.de/teaching/

Die "Hauptveranstaltung" Datenbanksysteme mit 6 CP geht bis Ende Dezember und
die 3CP Ergänzung Datenbanksysteme beginnt im Januar.
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-dbs.pdf

https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-ds.pdf
Der Kurs ist auf max. 50 Teilnehmer begrenzt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Das Seminar findet im Mevis Gebäude, Raum 0.10.C, statt.
https://lvb.informatik.uni-bremen.de/ims/03-ims-dlmb.pdf

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

https://lvb.informatik.uni-bremen.de/ibv/03-ibva-dsg.pdf

Schwerpunkt: IMVA-DMI, IMVA-AI
Abstract: The shift towards human-like interaction with computers through voice and text has made large language models like ChatGPT and Gemini a part of everyday life. While these open-domain chatbots are powerful, their "one-size-fits-all" approach often leads to user frustrations, including the need for overly specific prompts, irrelevant or verbose responses, and a lack of critical context.
Often, domain-specific interfaces are important for improving user experience. This offers the opportunity to build specific user interfaces or manipulate the language depending on the domain. We will explore how adapting natural language interfaces to specific fields (e.g., medical advice or creative writing) can resolve these common issues. In the seminar, students will investigate a specific research question, analyze relevant academic literature, and then design, implement, and evaluate a novel domain-specific user interface. The goal is to move beyond generic chatbots and create more intelligent, context-aware, and effective natural language tools.

We will work very hands-on and will have many sessions where we implement something. For the first session bring a laptop. You should have a GitLab account and Python (version 3.10 or later) installed on your computer. The course combines research with development, and each student has to give a presentation in the course and develop a prototype.

https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-eca.pdf
Für Studierende des Zertifikatsstudiums DiMePäd ist eine Teilnahme nur bei ausreichender Kapazität möglich.

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec1.pdf
Entertainment Computing I beschäftigt sich mit theoretischen Grundlagen, psychologischen Aspekten und methodischen Verfahren rund um digitale Spiele und interaktive Unterhaltungssysteme. Im Mittelpunkt stehen die Analyse und das Verständnis von Spielkonzepten, Nutzerverhalten und Evaluationsmethoden.

The kick-off meeting will take place on October 15h, 16:00 in room 2.43 of the Cartesium building. Time slots and locations of further sessions will be announced in the kick-off meeting.

In this course, you will take part in a large-scale data collection of biosignals, getting to hands-on experience scientific data collection, different sensors, and Virtual Reality scenarios. In the second half of the semester, you will be able to process and analyze your own data and reflect on your experience. You will learn about how dataset properties and experiment design decisions influence Machine Learning models.

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-gme.pdf
Termine: 10-tägiger Blockkurs nach der Vorlesungszeit.

Wird als hybrider Blockkurs nach der Vorlesungszeit angeboten. Bei Interesse frühzeitig bei felix.putzeprotect me ?!uni-bremenprotect me ?!.de anmelden, da begrenzte Teilnehmer:innen-Zahl. Eine Anmeldung nur über StudIP ist nicht ausreichend.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gpmlr.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-shar.pdf

Schwerpunkt: SQ
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-isec.pdf

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-iuag.pdf

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

https://lvb.informatik.uni-bremen.de/igs/03-imgs-rig.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 14.10.2025 um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

Profil: KIKR
Schwerpunkt: IMVP- AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mlar.pdf
Die Veranstaltung findet in den Räumen des DFKI statt.

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Schwerpunkt: DMI
https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-mti.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Dieser Blockkurs richtet sich in erster Linie an Studierende mit Vorkenntnissen in der hardware-nahen Programmierung von Mikrocontroller-Plattformen wie z. B. Arduino. Zur Auffrischung der Kenntnisse in der Programmiersprache C wird der vorherige Besuch des Propädeutikums C/C++ empfohlen.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-or.pdf

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ra.pdf

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: KIKR
Schwerpunkt: IMVP-VMC, IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-sprs.pdf
Die Vorlesung findet online per Zoom statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-srse.pdf

Schwerpunkt: VMC
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-sdv.pdf

• Was macht Sensordaten anders als anderen Daten?
• Kamera, Mikrophon, Inertialsensor als drei wichtige Sensoren und was sie messen
• Algorithmen zur Analyse von Sensordaten (z.B. Fouriertransformation, Sprachmerkmale, farbgetriebene BV, Filte, Houghtransformation, Bewegungsmerkmale, Klassifizierungsalgorithmen)
• Entwicklungs- und Evaluationsmethoden für Systeme mit Sensordaten
• Bayesschätzer und -filter

Die Vorlesung wird als Video zur Verfügung gestellt. In den Präsenzterminen wird der Stoff wiederholt und eingeübt. Dazu gibt es unbenotete Hausaufgaben als Vorbereitung. Die Prüfungsleistung ist eine Klausur.

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Technik hält in immer mehr Bereiche des Sports Einzug und wird unter anderem zur Leistungsdiagnostik, im Training sowie zur Analyse in der Nachbetrachtung eingesetzt. Dies erstreckt sich vom Profisport mittlerweile bis hinein in den Hobby-Bereich.
Dieser Kurs bietet einen Überblick über die aktuellen Teilbereiche der Sportinformatik und ihre Anwendungen. Es ist ein viertägiger Blockkurs. Die einzelnen Tage bestehen sowohl aus Vorlesungen sowie der Arbeit an einem kleinen praktischen Projekt, welches am Beispiel einer Sportübung den Prozess von der Datenaufnahme bis zur Analyse und Visualisierung nachvollzieht.

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-tcrs.pdf
Schwerpunkt: IMVP-AI

Die Übung findet in den Räumen der AG statt.

https://lvb.informatik.uni-bremen.de/igs/03-ibfw-vti1.pdf
Der Termin der Veranstaltung wird noch bekannt gegeben.

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

• • Dieser Kurs wird auf Englisch angeboten/ this course is held in English.

Im Sommersemester steht mit PMWK eine Alternative auf Deutsch zur Verfügung --

We discuss different aspects of science, academia and academic writing. At the same time, the course aims to help you understand your own role as a master student and job related goals. The course is both a preparation for studying at a master level and subsequent work in different roles in academia, business, the public sector or as an entrepreneur in Computer Science and Artificial Intelligence. Therefore, it is useful to take this course towards the beginning of your studies.

Students with the Subject "Master in Computer Science" can also complete the module "Projektmanagement und Wissenschaftskultur" with this course.

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

Schwerpunkt: SQ

Schwerpunkt: SQ, AI

Schwerpunkt: SQ

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-air.pdf
Die Veranstaltung findet im DFKI statt.

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

Profil: KIKR
Schwerpunkt: IMVP-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-bcod.pdf

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

Schwerpunkt: IMVA-DMI, IMVA-AI
Abstract: The shift towards human-like interaction with computers through voice and text has made large language models like ChatGPT and Gemini a part of everyday life. While these open-domain chatbots are powerful, their "one-size-fits-all" approach often leads to user frustrations, including the need for overly specific prompts, irrelevant or verbose responses, and a lack of critical context.
Often, domain-specific interfaces are important for improving user experience. This offers the opportunity to build specific user interfaces or manipulate the language depending on the domain. We will explore how adapting natural language interfaces to specific fields (e.g., medical advice or creative writing) can resolve these common issues. In the seminar, students will investigate a specific research question, analyze relevant academic literature, and then design, implement, and evaluate a novel domain-specific user interface. The goal is to move beyond generic chatbots and create more intelligent, context-aware, and effective natural language tools.

We will work very hands-on and will have many sessions where we implement something. For the first session bring a laptop. You should have a GitLab account and Python (version 3.10 or later) installed on your computer. The course combines research with development, and each student has to give a presentation in the course and develop a prototype.

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-gme.pdf
Termine: 10-tägiger Blockkurs nach der Vorlesungszeit.

Wird als hybrider Blockkurs nach der Vorlesungszeit angeboten. Bei Interesse frühzeitig bei felix.putzeprotect me ?!uni-bremenprotect me ?!.de anmelden, da begrenzte Teilnehmer:innen-Zahl. Eine Anmeldung nur über StudIP ist nicht ausreichend.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gpmlr.pdf

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

Profil: KIKR
Schwerpunkt: IMVP- AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mlar.pdf
Die Veranstaltung findet in den Räumen des DFKI statt.

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: KIKR
Schwerpunkt: IMVP-VMC, IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-sprs.pdf
Die Vorlesung findet online per Zoom statt.

Profil: SQ
Schwerpunkt: IMA-SQ, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-swre.pdf

Profil SQ
Schwerpunkt: IMK-SQ , IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-shsq.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-ta.pdf

https://lvb.informatik.uni-bremen.de/imva/03-imva-tmp.pdf
Schwerpunkt: IMVA-DMI
https://bernardrobben.net/wp-content/uploads/2023/03/TheMachinicProject.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-tcrs.pdf
Schwerpunkt: IMVP-AI

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdf
English or German.
Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets.

Some of the topics to be covered (tentatively):
• Introduction, basic notions of VR, several example applications
• VR technologies: displays, tracking, input devices, scene graphs, game engines
• The human visual system and Stereo rendering
• Techniques for real-time rendering
• Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control
• Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc.
• Particle systems
• Spring-mass systems
• Haptics and force feedback
• Collision detection
• Acoustic rendering
The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments.
You are encouraged to work on assignments in small teams.
https://cgvr.cs.uni-bremen.de/teaching/

https://lvb.informatik.uni-bremen.de/ims/03-ims-allm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Prior knowledge:
Basic knowledge of artificial intelligence (AI) and machine learning (ML), as acquired from
one of the introductory and foundational machine learning, AI, or cognitive systems lectures.
Advanced course knowledge, e.g. as acquired in "Life-long Machine Learning" or "Advanced
Machine Learning", is not required, but will be beneficial to engage with more recent in-depth
perspectives at the frontier of current research.

Learning Outcomes:
Traditional machine learning studies the design of models and training algorithms to learn to
solve tasks from data. In contrast to humans, who excel at adaptivity and benefit greatly from
learning curricula, machine learners are largely tailored to stationary data and static test
scenarios. Throughout the seminar, students will engage with state-of-the-art literature that
advances the frontiers of lifelong learning machines. They will improve their ability to
navigate scientific works, critically assess contributions and prospects, inspect the newest
algorithms, and identify persisting limitations and open research questions. In the process,
students will refine their scientific presentation skills and strengthen their competence in
participating in advanced machine learning discourse.

Content:
The seminar will cover different elements required to transform static machine learners into
lifelong learning machines. At the start of the seminar, students will get to select from a
choice of topics spanning a variety of modern perspectives and state-of-the-art lifelong
learning algorithms. In prospective in-depth engagement with the chosen literature as the
starting point, presentations will be prepared and held in front of the seminar participants to
fuel weekly scientific discourse. Discussed advances towards lifelong learning machines
encompass, but are not limited to:
* Memory: what consistutes memorable data points and episodes? How can we construct
concrete algorithms to identify and extract memorable experience? What types of
memories do humans possess and how do machine learning algorithms acommodate
them?
* Learning curricula: how can we identify suitable learning curricula for machine learning?
Which modern algorithms exist to enable models to benefit from structured curricula and
data streams?
* Continual optimization: how can we transcend the greediness of machine learning
optimizers? What algorithmic advances exist to effectively regularize past parameters,
representations, and knowledge?
* Adaptive and modular architectures: how can we dynamically modify opaque
architectures, such as deep neural networks? Which techniques exist to modularize
functions and disambiguate entangled representations?
* Lifelong ML theory: what are the advances in transferring machine learning theory to
lifelong machine learning? What are the insights and obstacles learned from grounded
advances analyzable, linear systems?
* Evaluation: How do we evaluate dimensions with inherent trade-offs, e.g. the pursuit of
stability and plasticity? How do we select hyperparameters in scenarios that are
constantly evolving? What techniques exist to enable fair comparisons across a
plethora of metrics of interest?

https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf

Das Seminar findet im Mevis Gebäude, Raum 0.10.C, statt.
https://lvb.informatik.uni-bremen.de/ims/03-ims-dlmb.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-shar.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-iuag.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 14.10.2025 um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

In this seminar, we learn the basics of bandit problems and algorithms, a framework for sequential decision tasks with exploration-exploitation tradeoff. We will see variations, extensions, and theoretical guarantees for the k-armed bandit problems and its applications in, e.g., reinforcement learning.

https://lvb.informatik.uni-bremen.de/ims/03-ims-srse.pdf

Nach der MPO 2020 ist diese LV ein Masterseminar, keine Master-Aufbau Veranstaltung.
https://lvb.informatik.uni-bremen.de/ims/03-ims-softc.pdf

https://lvb.informatik.uni-bremen.de/igs/03-ibfs-tstud.pdf
Zu Beginn jedes Semesters findet eine Infoveranstaltung statt, wo die Scheinkriterien für Versuchspersonen erläutert werden. Außerdem werden (aufbauend auf den Inhalten von WA1) Forschungsmethoden von Studien mit Versuchspersonen vermittelt.
Im weiteren Verlauf des Studiums sollen Studierende 15 Versuchspersonenstunden absolvieren (d.h. an mehreren Studien teilnehmen). Jede Studienteilnahme wird mit Versuchspersonenstunden vergütet, die in ECTS anerkannt werden können. Die Versuchspersonenstunden können über mehre Semester gesammelt werden.
Die Teilnahme an den Studien soll in einer schriftlichen Ausarbeitung dokumentiert und reflektiert werden.



At the beginning of each semester, an information session is held where the certificate criteria for subjects are explained. In addition, research methods of studies with test subjects are taught (building on the contents of WA1).

Later in the program, students are expected to complete 15 subject hours (i.e., participate in different studies). Each study participation is compensated with subject hours, which can be recognized in ECTS. The subject hours may be accumulated over multiple semesters.

Participation in the studies should be documented and reflected upon in a written paper at the end.
The selection of participants is made manually after registration.

Users who wish to register for this event will receive more detailed information and can then still decide against participation.

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

https://lvb.informatik.uni-bremen.de/imap/03-imap-aswe.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-allm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Prior knowledge:
Basic knowledge of artificial intelligence (AI) and machine learning (ML), as acquired from
one of the introductory and foundational machine learning, AI, or cognitive systems lectures.
Advanced course knowledge, e.g. as acquired in "Life-long Machine Learning" or "Advanced
Machine Learning", is not required, but will be beneficial to engage with more recent in-depth
perspectives at the frontier of current research.

Learning Outcomes:
Traditional machine learning studies the design of models and training algorithms to learn to
solve tasks from data. In contrast to humans, who excel at adaptivity and benefit greatly from
learning curricula, machine learners are largely tailored to stationary data and static test
scenarios. Throughout the seminar, students will engage with state-of-the-art literature that
advances the frontiers of lifelong learning machines. They will improve their ability to
navigate scientific works, critically assess contributions and prospects, inspect the newest
algorithms, and identify persisting limitations and open research questions. In the process,
students will refine their scientific presentation skills and strengthen their competence in
participating in advanced machine learning discourse.

Content:
The seminar will cover different elements required to transform static machine learners into
lifelong learning machines. At the start of the seminar, students will get to select from a
choice of topics spanning a variety of modern perspectives and state-of-the-art lifelong
learning algorithms. In prospective in-depth engagement with the chosen literature as the
starting point, presentations will be prepared and held in front of the seminar participants to
fuel weekly scientific discourse. Discussed advances towards lifelong learning machines
encompass, but are not limited to:
* Memory: what consistutes memorable data points and episodes? How can we construct
concrete algorithms to identify and extract memorable experience? What types of
memories do humans possess and how do machine learning algorithms acommodate
them?
* Learning curricula: how can we identify suitable learning curricula for machine learning?
Which modern algorithms exist to enable models to benefit from structured curricula and
data streams?
* Continual optimization: how can we transcend the greediness of machine learning
optimizers? What algorithmic advances exist to effectively regularize past parameters,
representations, and knowledge?
* Adaptive and modular architectures: how can we dynamically modify opaque
architectures, such as deep neural networks? Which techniques exist to modularize
functions and disambiguate entangled representations?
* Lifelong ML theory: what are the advances in transferring machine learning theory to
lifelong machine learning? What are the insights and obstacles learned from grounded
advances analyzable, linear systems?
* Evaluation: How do we evaluate dimensions with inherent trade-offs, e.g. the pursuit of
stability and plasticity? How do we select hyperparameters in scenarios that are
constantly evolving? What techniques exist to enable fair comparisons across a
plethora of metrics of interest?

Schwerpunkt: IMVT-AI

Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-air.pdf
Die Veranstaltung findet im DFKI statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-ata.pdf

Profil KIKR
Schwerpunkt: IMA-AI
Modultyp C (6 CP) im Studiengang Language Sciences M.A.
https://lvb.informatik.uni-bremen.de/imap/03-imap-ase.pdf

Die Vorlesungsinhalte werden über Videos und Folien asynchron bereitgestellt ("flipped classroom"-Konzept). Darüber hinaus gibt es einen Präsenz-Termin am Dienstag. Dieser Präsenz-Termin ist als "Interaktives Repititorium" gestaltet und wird nicht aufgezeichnet. Donnerstags finden im Zwei-Wochen-Rhythmus Übungen statt.

Der Kurs "Automatische Spracherkennung" bietet eine Einführung in die automatische Spracherkennung. In diesem Kurs werden die Sprachverarbeitung beim Menschen, Signalverarbeitung, statistische Modellierung von Sprache sowie die wesentlichen praktischen Ansätze und Methoden für den Einsatz automatischer Spracherkennung behandelt.

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-bs.pdf

Profil: KIKR
Schwerpunkt: IMVP-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-bcod.pdf

Schwerpunkt: DMI, VMC
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-cg.pdf
Programmierkenntnisse sind Voraussetzung (ein erfolgreicher Abschluss des "Propädeutikums C" wird empfohlen), ebenso wie algorithmisches Denken, eine gewisse Vertrautheit mit mathematischer Begriffsbildung und Vorgehensweise.

Diese Vorlesung soll sowohl eine Einführung in die theoretischen und methodischen Grundlagen der Computergraphik geben, als auch die Grundlagen für die praktische Implementierung von computergraphischen Systemen legen. Der Schwerpunkt liegt auf Algorithmen und Konzepten zur Repräsentation und Visualisierung von polygonalen, 3-dimensionalen graphischen Szenen.
Der Inhalt umfasst in der Regel folgende Themen:
Mathematische Grundlagen; OpenGL and C ; 2D Algorithmen der Computergrafik (Scan Conversion, Visibility Computations, etc.); Theorie der Farben, Farbräume (hauptsächlich physikalische, neurologische, und technische Aspekte); 3D Computergraphik (Rendering Pipeline, Transformationen, Beleuchtung, etc.); Techniken zum Echtzeit-Rendering; Das Konzept und die Programmierung von Shadern; Texturierung (Einordnung in die Pipeline, einfache Parametrisierung, etc.).

Die Übungsaufgaben werden teils theoretisch, teils praktisch sein, wobei die praktischen Aufgaben gewisse Programmierfähigkeiten in C verlangen. (Zu Beginn der Vorlesung wird deshalb nochmals ein kurzer "Refresh" Ihrer C/C-Kenntnisse gemacht.) Die Vorlesung setzt eine gewisse mathematische, algorithmische und programmiertechnische Gewandtheit voraus, fördert diese aber auch und führt sie weiter.
https://cgvr.cs.uni-bremen.de/teaching/

Die "Hauptveranstaltung" Datenbanksysteme mit 6 CP geht bis Ende Dezember und
die 3CP Ergänzung Datenbanksysteme beginnt im Januar.
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-dbs.pdf

https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-ds.pdf
Der Kurs ist auf max. 50 Teilnehmer begrenzt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models

Das Seminar findet im Mevis Gebäude, Raum 0.10.C, statt.
https://lvb.informatik.uni-bremen.de/ims/03-ims-dlmb.pdf

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf
Die Übung der Veranstaltung findet am Dienstag in Raum MZH 2340 statt.

https://lvb.informatik.uni-bremen.de/ibv/03-ibva-dsg.pdf

Schwerpunkt: IMVA-DMI, IMVA-AI
Abstract: The shift towards human-like interaction with computers through voice and text has made large language models like ChatGPT and Gemini a part of everyday life. While these open-domain chatbots are powerful, their "one-size-fits-all" approach often leads to user frustrations, including the need for overly specific prompts, irrelevant or verbose responses, and a lack of critical context.
Often, domain-specific interfaces are important for improving user experience. This offers the opportunity to build specific user interfaces or manipulate the language depending on the domain. We will explore how adapting natural language interfaces to specific fields (e.g., medical advice or creative writing) can resolve these common issues. In the seminar, students will investigate a specific research question, analyze relevant academic literature, and then design, implement, and evaluate a novel domain-specific user interface. The goal is to move beyond generic chatbots and create more intelligent, context-aware, and effective natural language tools.

We will work very hands-on and will have many sessions where we implement something. For the first session bring a laptop. You should have a GitLab account and Python (version 3.10 or later) installed on your computer. The course combines research with development, and each student has to give a presentation in the course and develop a prototype.

https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-eca.pdf
Für Studierende des Zertifikatsstudiums DiMePäd ist eine Teilnahme nur bei ausreichender Kapazität möglich.

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Profil: SQ
Schwerpunkt: IMA-SQ.
https://lvb.informatik.uni-bremen.de/imat/03-imat-krypt.pdf
mit Zusatzleistung(en) (wird in der Veranstaltung bekannt gegeben) 9 CP für Studierende in den mathematischen Studiengängen

Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec1.pdf
Entertainment Computing I beschäftigt sich mit theoretischen Grundlagen, psychologischen Aspekten und methodischen Verfahren rund um digitale Spiele und interaktive Unterhaltungssysteme. Im Mittelpunkt stehen die Analyse und das Verständnis von Spielkonzepten, Nutzerverhalten und Evaluationsmethoden.

The kick-off meeting will take place on October 15h, 16:00 in room 2.43 of the Cartesium building. Time slots and locations of further sessions will be announced in the kick-off meeting.

In this course, you will take part in a large-scale data collection of biosignals, getting to hands-on experience scientific data collection, different sensors, and Virtual Reality scenarios. In the second half of the semester, you will be able to process and analyze your own data and reflect on your experience. You will learn about how dataset properties and experiment design decisions influence Machine Learning models.

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-gme.pdf
Termine: 10-tägiger Blockkurs nach der Vorlesungszeit.

Wird als hybrider Blockkurs nach der Vorlesungszeit angeboten. Bei Interesse frühzeitig bei felix.putzeprotect me ?!uni-bremenprotect me ?!.de anmelden, da begrenzte Teilnehmer:innen-Zahl. Eine Anmeldung nur über StudIP ist nicht ausreichend.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gpmlr.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-shar.pdf

Schwerpunkt: SQ
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-isec.pdf

Profil: KIKR
Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-iis.pdf
Die Vorlesung findet asynchron und die Übung online statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-iuag.pdf

Profil: SQ
Schwerpunkt: IMVT-SQ
https://lvb.informatik.uni-bremen.de/imat/03-imat-irq.pdf

Profil: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-itmds.pdf

https://lvb.informatik.uni-bremen.de/igs/03-imgs-rig.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 14.10.2025 um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

Profil: KIKR
Schwerpunkt: IMVP- AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mlar.pdf
Die Veranstaltung findet in den Räumen des DFKI statt.

Profil: SQ, DMI.
Schwerpunkt: IMA-DMI, IMVA-SQ
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-mitr.pdf

Schwerpunkt: DMI
https://lvb.informatik.uni-bremen.de/ibaa/03-ibaa-mti.pdf

Profil: DMI
Schwerpunkt: IMA- DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Student*innen der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Dieser Blockkurs richtet sich in erster Linie an Studierende mit Vorkenntnissen in der hardware-nahen Programmierung von Mikrocontroller-Plattformen wie z. B. Arduino. Zur Auffrischung der Kenntnisse in der Programmiersprache C wird der vorherige Besuch des Propädeutikums C/C++ empfohlen.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-or.pdf

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ra.pdf

Profil: SQ, DMI.
Schwerpunkt: IMVP-SQ, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rnmn.pdf

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-stmw.pdf

Profil: KIKR
Schwerpunkt: IMVP-VMC, IMVP-AI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-sprs.pdf
Die Vorlesung findet online per Zoom statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-srse.pdf