Die Vorlesung findet online asynchron statt.
Die Präsenz-Übungen am Donnerstag finden im Raum RH1 B0.10 des DFKI statt.

Profil: AI
Die Veranstaltung findet im TAB statt.

Profil: SQ
Schwerpunkt: IMAT-SQ, IMAT-AI

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: IMVT-SQ

Set theory and model theory

Intuitively, a set is a collection of all elements that satisfy a certain property. This intuition, however, is false! The following example is known as Russell's Paradox. Consider the set S whose elements are exactly those that are not members of themselves: S = { X : X is not element of X }. Is S an element of S? If S is an element of S, then S is not an element of S. On the other hand, if S is not an element of S, then S belongs to S. In either case we have a contradiction. We must revise our intuitive notion of a set. In the first part of the lecture we develop axiomatic set theory (ZFC) in the framework of first-order logic, which forms the foundation of modern mathematics. We cover the axioms of set theory, ordinal numbers and induction and recursion over well-founded relations, cardinal numbers and the axiom of choice.

In the second part of the lecture we turn to classical topics of first-order model theory. Model theory studies classes of mathematical structures, such as groups, fields, or graphs, from the point of view of mathematical logic. Many notions, such as homomorphisms, substructures, or free structures, that are commonly studied in specific fields of mathematics are unified by the general approach of model theory. We study ways to construct models with desired properties from first-order theories and the expressive power of first-order logic.

Profil: KIKR
Schwerpunkt: IMAP-AI
Die Vorlesung findet asynchron und die Übung online statt.

Profil: KIKR, DMI
Schwerpunkt: IMAP-DMI, IMAP-VMC.
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: SQ, DMI
Schwerpunkt: IMA-SQ, IMA-AI, IMVA-DMI
Die Veranstaltung beginnt am 25.10.2022

Profil: KIKR, DMI
Schwerpunkt: IMVA-DMI, IMVA-AI

Das Seminar beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Solche Daten fallen in unterschiedlichsten wissenschaftlichen Anwendungen an. Sie entstehen zum einen durch Simulationen auf Hochleistungsrechnern (z.\ B. zur Unterstützung der Klimaforschung oder für die Vorhersage von Umströmung von Flugzeugflügeln). Sie können aber auch durch Messungen, wie bspw. durch Erdbeobachtungsmissionen, erzeugt werden. Um überhaupt erst aussagekräftige Informationen für die Visualisierung zu erhalten, müssen diese enorm großen Rohdaten zunächst prozessiert werden. Für eine anschließende explorative Analyse werden echtzeitfähige, interaktive Methoden benötigt, die wiederum auf hochparallele und effiziente Verfahren beruhen. Das Seminar greift daher aktuelle Trends in der wissenschaftlichen Visualisierung auf. Zur Auswahl stehen herausragende Publikationen führender Wissenschaftler, die Themen von Multi-Resolution-Extraktion von Toplologiemerkmalen bis hin zu parallelen Beschleunigungsverfahren für das Volumenrendering in virtuellen Arbeitsumgebungen behandeln.

Many every-day processes can seen as a game between autonomous interacting players, where each player acts stategically in order to pursue her own objectives. This lecture is an introduction to game-theoretic concepts and techniques, mainly with connections to applications. Use-cases are distributed systems, auctions, online-markets, resource allocation, traffic routing, and sports. The goal of the lecture is to provide an overview over state-of-the-art results in the area of algorithmic game theory. Main topics that we will cover in the course are games in normal form, efficiency of equilibria, auctions, truthfulness and VCG-mechanisms, social choice, cake cutting, and cooperative games.

The lectures and homework sheets will be in English language. If all participants agree, the exercise session could be held in German. If there is an oral exam, the language can be chosen by the candidate. In case of a written exam the questions will be in English, answering them in German or English is fine.

Profil: KIKR
Schwerpunkt:IMVP- AI
Die Vorlesung findet asynchron per Video Lectures statt.

Schwerpunkt: IMVP-SQ

4 SWS, Profil: DMI
Schwerpunkt: IMVA-DMI
Kurzbeschreibung unter: http://dimeb.informatik.uni-bremen.de/FabLab/GenerativeDesign.pdf
Präsenz-Meeting mittwochs 14-16h und anschließend 2 Std Übungen im FabLab wöchentlich.

Profil: DMI
Schwerpunkt:IMVA- DMI

Die Veranstaltung richtet sich an Studenten 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.

Veranstaltung für Doktoranten, jeden 1. Montag im Monat von 14-16h in Raum 5300.