Der Brückenkurs findet in der Zeit vom 20.09.2021 bis 01.10.2020 statt.
Für neu beginnende Studenten und Studentinnen in allen (Techno-)Mathe- und Informatik-Studiengängen des Fachbereiches 3.
Anmeldungsdetails inden Sie auf
http://www.math.uni-bremen.de/brueckenmathematik
Die Rauminformationen kommen nach der Anmeldung.
Die Vorlesungen findet täglich von 10:00 - 11:30 Uhr im NW2 C0290 und die Übungen täglich von 12:30 - 14:30 Uhr in den Räumen MZH 1090, MZH 1100, MZH 1110, MZH 1450, MZH 1460, MZH 1470 statt.

Für Studierende des Vollfachs Informatik, Systems Engineering und Wirtschaftinformatik. Für Studierende der Digitalen Medien und Komplementärfach Informatik gibt es die Veranstaltung Grundlagen der Programmierung.
Die Studierenden der Beruflichen Bildung - Mechatronik besuchen PI1 anstatt Grundlagen der Programmierung mit einer reduzierten CP-Anzahl, nämlich 6 CP durch reduzierten Übungsbetrieb.
Die Übungen finden online statt.

Aktuelle Informationsbroschüren finden Sie unter www.szmathe.uni-bremen.de

Die algorithmische diskrete Mathematik ist ein recht junges Gebiet mit Wurzeln in der Algebra, Graphentheorie, Kombinatorik, Informatik (Algorithmik) und Optimierung. Sie behandelt diskrete Strukturen wie Mengen, Graphen, Permutationen, Partitionen und diskrete Optimierungsprobleme.

Diese Veranstaltung gibt eine Einführung in die algorithmische diskrete Mathematik. Es werden strukturelle und algorithmische Grundlagen der Graphentheorie und kombinatorischen Optimierung vermittelt. Im Vordergrund steht die Entwicklung und mathematische Analyse von Algorithmen zum exakten Lösen von kombinatorischen Optimierungsproblemen. Es werden u.a. folgende Themen behandelt:

* Einführung in Graphentheorie, kombinatorische und lineare Optimierung
* Graphentheorie: Grundbegriffe, Wege in Graphen, Euler- und Hamiltonkreise, Bäume
* Algorithmische Grundlagen (Kodierungslänge, Laufzeit, Polynomialzeitalgorithmen)
* Spannbäume, Matchings, Netzwerkflüsse und -schnitte (kombinatorische Algorithmen)
* Einblick in lineare Optimierung: Modellierung, Polyedertheorie, Optimalitätskriterien, Dualität
* Elemente der Komplexitätstheorie

Die Veranstaltung richtet sich vorrangig an fortgeschrittene Bachelorstudierende, ist aber auch für Masterstudierende geeignet.

6 SWS setzen sich zusammen aus
* 2 SWS Vorlesung asynchron/video + material; Termine für Diskussion und Austausch
* 2 SWS interaktive Übung
* 2 SWS seminaristischer Anteil, Eigenstudium eines aktuellen Forschungsartikels und Vortrag

Erster Termin zur Einführung und organisatorischen Absprachen: am Do 21.10., 10-12, im MZH 3150.
Hier werden auch die Themen und der Ablauf für den Seminaranteil besprochen.

2x 2SWS Vorlesung und 2SWS Übung.
Studiengänge: M-BM-Alg

Homepage zur Veranstaltung: http://www.feb.uni-bremen.de
Termine nach Vereinbarung

Proseminar findet als Blockveranstaltung am Ende des Wintersemesters 2021/22 statt.

Es handelt sich um ein kombiniertes Proseminar und Seminar und diese Veranstaltung ist sowohl für Bachelor als auch Masterstudierende geeignet. Das erste Treffen und die Themenverteilung ist am Mittwoch, 20.10.2021, 12:00 - 14:00 Uhr. Danach finden zu Semesterbeginn einige wöchentliche Termine statt. Die Abschlussvorträge sind im Rahmen einer Blockveranstaltung Mitte März.

Aktuelle Informationsbroschüren finden Sie unter www.szmathe.uni-bremen.de

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-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.

2x 2SWS Vorlesung und 2SWS Übung.
Studiengänge: M-BM-Alg

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-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.

Schwerpunkt: Ai

Homepage zur Veranstaltung: http://www.math.uni-bremen.de/~schmi/WS21/numpde.html

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.

This course is as 2 hours lecture + 1 hour of exercise class = 3SWS = 4,5 CP , meaning ”a half module”. Both take place online via BigBlueButton and records (live broadcast) and the examination is a written exam. Also, there will be a follow-up course (again in the format of a ”half module”) in the next semester which is related to frequency domain analyses of univariate and multivariate time series (Chapters 11 to 20 of the reference book). The two courses can be combined to form one full 9 CP module.

This seminar focuses on recent research in the broad area of robust optimization and highlights various techniques to handle different types of uncertainty.

Many combinatorial optimization problems do not consider that the input data of real-world applications may be uncertain. One approach to tackle this uncertainty is robust optimization, in which we optimize the worst-case of all possible realizations of the input data. However, depending on the application, the uncertainty can be expressed in many different ways:
the cost of the resources, the structure of the problem affecting the set of feasible solutions, or even which precise constraints we have to satisfy.
We will study different approaches on handling these kind of uncertainties for robust counterparts of some classical combinatorial optimization problems such as shortest path, minimum spanning tree or the assignment problem.

Format:

The seminar aims at Master's students; Bachelor's students in higher semesters are also welcome. Students are expected to read and thoroughly understand original research papers, and to deliver an oral presentation and a write up.

The first meeting is on Wednesday, October 20, at 10:15 am, room MZH 1100. We will discuss the organization as well as intermediate meetings and allocate the research articles.

Please register with StudIP!

We intend to schedule the talks as a two-day block seminar during the first week after the end of the semester (Feb 7-11, 2022). We will discuss this in the first zoom meeting.

Proseminar findet als Blockveranstaltung am Ende des Wintersemesters 2021/22 statt.

This seminar focuses on recent research in the broad area of robust optimization and highlights various techniques to handle different types of uncertainty.

Many combinatorial optimization problems do not consider that the input data of real-world applications may be uncertain. One approach to tackle this uncertainty is robust optimization, in which we optimize the worst-case of all possible realizations of the input data. However, depending on the application, the uncertainty can be expressed in many different ways:
the cost of the resources, the structure of the problem affecting the set of feasible solutions, or even which precise constraints we have to satisfy.
We will study different approaches on handling these kind of uncertainties for robust counterparts of some classical combinatorial optimization problems such as shortest path, minimum spanning tree or the assignment problem.

Format:

The seminar aims at Master's students; Bachelor's students in higher semesters are also welcome. Students are expected to read and thoroughly understand original research papers, and to deliver an oral presentation and a write up.

The first meeting is on Wednesday, October 20, at 10:15 am, room MZH 1100. We will discuss the organization as well as intermediate meetings and allocate the research articles.

Please register with StudIP!

We intend to schedule the talks as a two-day block seminar during the first week after the end of the semester (Feb 7-11, 2022). We will discuss this in the first zoom meeting.

Weitere Informationen finden Sie auf
http://www.math.uni-bremen.de/~schmi/WS21/semnumpde.html

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.

Es handelt sich um ein kombiniertes Proseminar und Seminar und diese Veranstaltung ist sowohl für Bachelor als auch Masterstudierende geeignet. Das erste Treffen und die Themenverteilung ist am Mittwoch, 20.10.2021, 12:00 - 14:00 Uhr. Danach finden zu Semesterbeginn einige wöchentliche Termine statt. Die Abschlussvorträge sind im Rahmen einer Blockveranstaltung Mitte März.

Homepage zur Veranstaltung: http://anstat.uni-bremen.de/node/6

Weitere Infos auf der Seminar-Homepage

Termin nach Vereinbarung.
Studiengang: T-M

Hompage dieser Veranstaltung: http://www.math.uni-bremen.de/rtg-pi3/qualification/rtgseminar

Homepage zur Veranstaltung: http://zetem.uni-bremen.de/o2c/veranstaltungen

The course provides an introductory level of programming skills in R.
Students are welcome to present own ideas, data and projects. I expect a project report or a method talk with demo on own data. Practicals in "R" will work also on synthetic data to illustrate methods features, limitations and differences.