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Course Catalog

Study Program WiSe 2021/2022

Mathematik B.Sc./M.Sc.

Veranstaltungen vor dem 1. Semester

Course numberTitle of eventLecturer
03-M-BM-1BrückenMathematik
Bridge-Course Mathematics at the University Bremen
- Brückenkurs Mathematik an der Universität Bremen -

Blockveranstaltung (Teaching)

Additional dates:
Mon. 20.09.21 - Fri. 24.09.21 (Mon., Tue., Wed., Thu., Fri.) 10:00 - 14:30
Mon. 27.09.21 - Fri. 01.10.21 (Mon., Tue., Wed., Thu., Fri.) 10:00 - 14:30

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.

Dr. Ingolf Schäfer
Prof. Dr. Thorsten-Ingo Dickhaus
Prof. Dr. Andreas Rademacher
Prof. Dr. Jens Rademacher
Prof. Dr. Daniel Schmand
Prof. Dr. Marc Keßeböhmer
Eugenia Saorin Gomez
PD Dr. Hendrik Vogt
Erik Hanke
Alfred Schmidt
Lars Siemer

Bachelor: Pflichtveranstaltungen

Pflichtveranstaltungen für den Studiengang Mathematik B.Sc.
Course numberTitle of eventLecturer
03-IBGP-PI1Praktische Informatik 1: Imperative Programmierung und Objektorientierung
Practical Computer Science 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 14:00 - 16:00 HS 2010 (Großer Hörsaal) Vorlesung
weekly (starts in week: 1) Wed. 10:00 - 13:00 Übung Online
weekly (starts in week: 1) Wed. 13:00 - 16:00 Übung Online
weekly (starts in week: 1) Thu. 08:00 - 10:00 HS 2010 (Großer Hörsaal) Vorlesung
weekly (starts in week: 1) Fri. 08:00 - 11:00 Übung Online
weekly (starts in week: 1) Fri. 11:00 - 14:00 Übung Online
weekly (starts in week: 1) Fri. 14:00 - 17:00 Übung Online

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.

Thomas Röfer
03-M-ALG-1Algebra

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 1090 Vorlesung
weekly (starts in week: 1) Tue. 12:00 - 14:00 GW2 B1410
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 2490 (Seminarraum) Übung Präsenz
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 1090 Vorlesung
Dr. Tim Haga
03-M-ANA-1.1Analysis 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 2) Tue. 09:00 - 10:00 MZH 3150 Besprechungstermin
weekly (starts in week: 1) Tue. 10:00 - 12:00 HS 1010 (Kleiner Hörsaal) Vorlesung
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 3150 Übung
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 7200 Übung
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 4140 Übung Präsenz
weekly (starts in week: 1) Wed. 08:00 - 10:00 MZH 2340 Übung
weekly (starts in week: 1) Wed. 08:00 - 10:00 MZH 4140 Übung
weekly (starts in week: 1) Wed. 16:00 - 18:00 MZH 4140 Übung
weekly (starts in week: 1) Fri. 08:00 - 10:00 HS 1010 (Kleiner Hörsaal) Vorlesung
Prof. Dr. Anke Dorothea Pohl
03-M-ANA-1.2Vertiefung zur Analysis 1 für Vollfach
Additional Topics in Calculus 1

Projektplenum (Teaching)
ECTS: 1,5

Dates:
weekly (starts in week: 1) Thu. 16:00 - 18:00 MZH 1380/1400 Plenum
Prof. Dr. Anke Dorothea Pohl
03-M-ANA-3Analysis 3

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1090 Vorlesung
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2490 (Seminarraum) Übung
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 1090 Vorlesung
Prof. Dr. Jens Rademacher
03-M-COM-1Computerpraktikum
Computer Laboratory

Kurs (Teaching)
ECTS: 3
Michael Eden
03-M-LAG-1.1Lineare Algebra 1
Linear Algebra 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 HS 1010 (Kleiner Hörsaal) Vorlesung
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 3150 Übung
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 4140 Übung
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 4140 Übung
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 2340 Übung
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 4140 Übung
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 4140 Übung
weekly (starts in week: 1) Thu. 14:00 - 16:00 GW2 B1820 GEO 1550 (Hörsaal) Vorlesung
Eugenia Saorin Gomez
03-M-LAG-1.2Vertiefung zur Linearen Algebra 1 für Vollfach
Additional Topics in Linear Algebra 1

Projektplenum (Teaching)
ECTS: 1,5

Dates:
weekly (starts in week: 1) Thu. 12:00 - 14:00 GW2 B1410 Plenum
Eugenia Saorin Gomez
03-M-NUM-1Numerik 1
Numerical Analysis 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 2340 Übung
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 1380/1400 Vorlesung
weekly (starts in week: 1) Wed. 08:00 - 10:00 Vorlesung
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 1110 Übung
Prof. Dr. Christof Büskens

Bachelor: Wahlpflichtveranstaltungen

Wahlpflichtveranstaltungen für den Studiengang Mathematik B.Sc.
Course numberTitle of eventLecturer
03-M-Gy4-1Funktionentheorie
Complex Analysis

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 1380/1400 Übung
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 1090 Vorlesung
weekly (starts in week: 1) Mon. 16:00 - 18:00 MZH 1380/1400 Übung
weekly (starts in week: 1) Fri. 08:00 - 10:00 SFG 2010 Vorlesung

Additional dates:
Mon. 07.02.22 13:00 - 16:00 MZH 1110
Mon. 07.02.22 13:00 - 16:00 MZH 1380/1400
Dr. Ingolf Schäfer
Prof. Dr. Christian Rose
03-M-MM-1Mathematische Modellierung
Mathematical Modelling

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2340 Vorlesung
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 3150 Vorlesung
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 3150 Übung
Prof. Dr. Andreas Rademacher
03-M-VOR-1Vorstellung der Mathematik-Lehrveranstaltungen im WiSe 2021/22
Presentation of Upper-Level Math Courses

Blockeinheit (Teaching)

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

Prof. Dr. Thorsten-Ingo Dickhaus
03-M-WP-11Maß- und Wahrscheinlichkeitstheorie
Measure Theory and Probability 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 3150 Übung
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 4140 Vorlesung
Prof. Dr. Marc Keßeböhmer
03-M-WP-18Algorithmische Diskrete Mathematik
Algorithmic Discrete Mathematics

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 3150 Vorlesung
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 3150 Übung

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.

Prof. Dr. Nicole Megow
03-M-WP-46Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 Vorlesung
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 Übung
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 7200 Vorlesung

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

Prof. Dr. Dmitry Feichtner-Kozlov

Bachelor: Proseminare

Course numberTitle of eventLecturer
03-M-PS-1Forschungserfahrungen im Bachelor
Research Experiences for Undergraduates

introductory seminar course (Teaching)
ECTS: 5

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

Prof. Dr. Marc Keßeböhmer
Prof. Dr. Jens Rademacher
03-M-PS-16Stochastik

introductory seminar course (Teaching)
ECTS: 5

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 4140 Seminar
Prof. Dr. Marc Keßeböhmer
03-M-PS-17Proseminar zur Linearen Algebra
33 Miniaturen zur Linearen Algebra

introductory seminar course (Teaching)
ECTS: 5

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 7200 MZH 1450
Eugenia Saorin Gomez
03-M-PS-18Einführung in Analysis auf Graphen

introductory seminar course (Teaching)
ECTS: 5

Additional dates:
Mon. 18.10.21 10:00 - 12:00 MZH 3150

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

Prof. Dr. Christian Rose
03-M-SEM-26Diskrete Optimierung
Seminar und Proseminar

Seminar (Teaching)
ECTS: 6 (5)

Dates:
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Seminar/Proseminar

Additional dates:
Tue. 15.03.22 - Wed. 16.03.22 (Tue., Wed.) 08:00 - 18:00 MZH 1470

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.

Prof. Dr. Daniel Schmand
03-M-SEM-28Informationstheorie (Seminar/ Proseminar)

Seminar (Teaching)
ECTS: 6(5)

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 1110 Seminar
Prof. Dr. Thorsten-Ingo Dickhaus

Master: Wahlpflichtveranstaltungen

Course numberTitle of eventLecturer
03-M-VOR-1Vorstellung der Mathematik-Lehrveranstaltungen im WiSe 2021/22
Presentation of Upper-Level Math Courses

Blockeinheit (Teaching)

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

Prof. Dr. Thorsten-Ingo Dickhaus

Vertiefungsrichtung Algebra

Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1450 Kurs Präsenz
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 3150 Kurs Präsenz

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.

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim
03-M-WP-46Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 Vorlesung
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 Übung
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 7200 Vorlesung

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

Prof. Dr. Dmitry Feichtner-Kozlov

Vertiefungsrichtung Analysis

Course numberTitle of eventLecturer
03-M-WP-11Maß- und Wahrscheinlichkeitstheorie
Measure Theory and Probability 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 3150 Übung
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 4140 Vorlesung
Prof. Dr. Marc Keßeböhmer
03-M-WP-55Semiclassical Analysis

Lecture (Teaching)
ECTS: 4,5

Dates:
weekly (starts in week: 1) Mon. 16:00 - 18:00 MZH 2490 (Seminarraum) Vorlesung
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 4140 Vorlesung und Übung
Dr. Moritz Doll
03-M-WP-57Mathematische Methoden der Signal- und Bildverarbeitung

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Vorlesung
weekly (starts in week: 1) Fri. 08:00 - 10:00 MZH 2340 Übung
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Vorlesung
Peter Maaß
Dr. Matthias Beckmann

Vertiefungsrichtung Numerik

Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1450 Kurs Präsenz
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 3150 Kurs Präsenz

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.

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim
03-IMVP-IMLInterpretable Machine Learning (in English)

Kurs (Teaching)
ECTS: 6 (optional +3)

Dates:
weekly (starts in week: 3) Wed. 16:15 - 17:45 Kurs online
weekly (starts in week: 2) Thu. 08:15 - 09:45 Kurs online

Schwerpunkt: Ai

Prof. Dr. Marvin Nils Ole Wright
03-M-PDE-1Numerik partieller Differentialgleichungen
Numerical Methods for Partial Differential Equations

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 2340 Vorlesung
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 1100 Übung
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 2340 Vorlesung


Alfred Schmidt
03-M-WP-28Algorithmic Game Theory (in English)

Lecture (Teaching)
ECTS: 9 (6)

Dates:
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Vorlesung
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 2340 Übung
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 2340 Vorlesung

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.

Prof. Dr. Daniel Schmand
03-M-WP-57Mathematische Methoden der Signal- und Bildverarbeitung

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Vorlesung
weekly (starts in week: 1) Fri. 08:00 - 10:00 MZH 2340 Übung
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Vorlesung
Peter Maaß
Dr. Matthias Beckmann
04-M30-CP-SFT-3Trajectory Optimization (in English)

Lecture (Teaching)
ECTS: 4,5

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 HS 2010 (Großer Hörsaal)
Matthias Knauer
Prof. Dr. Christof Büskens

Vertiefungsrichtung Stochastik & Statistik

Course numberTitle of eventLecturer
03-M-WP-11Maß- und Wahrscheinlichkeitstheorie
Measure Theory and Probability 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 3150 Übung
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 4140 Vorlesung
Prof. Dr. Marc Keßeböhmer
03-M-WP-15Statistik 1
Statistics 1

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1110 Übung
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1110 Vorlesung
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 1110 Vorlesung
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-WP-17Statistik 3 (Generalisierte Lineare Modelle)
Generalised Linear Models in Statistics

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 1100 Vorlesung
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 2340 Übung
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 1100 Vorlesung
Prof. Dr. Werner Brannath
03-M-WP-58Time Series Analysis, Univariate and Multivariate Methods (in English)

Lecture (Teaching)
ECTS: 4,5

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 Vorlesung online
fortnightly (starts in week: 2) Wed. 12:00 - 14:00 Übung Online

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.

Maryam Movahedifar

Master: Seminare

Vertiefungsrichtung Algebra

Course numberTitle of eventLecturer
03-IMS-HROHighlights of Robust Optimization

Seminar (Teaching)
ECTS: 3 (auch als 6 möglich)

Additional dates:
Wed. 20.10.21 10:00 - 12:00 MZH 1100
Wed. 24.11.21 10:00 - 12:00 MZH 2490 (Seminarraum)

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.

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim

Vertiefungsrichtung Analysis

Course numberTitle of eventLecturer
03-M-PS-18Einführung in Analysis auf Graphen

introductory seminar course (Teaching)
ECTS: 5

Additional dates:
Mon. 18.10.21 10:00 - 12:00 MZH 3150

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

Prof. Dr. Christian Rose
03-M-SEM-29Ergodentheorie
(Undergraduate) Seminar Ergodic Theory

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 7200 Übung Präsenz
Prof. Dr. Anke Dorothea Pohl
03-M-SEM-31Spektraltheorie für Operatoren in Banachräumen
Spectral Theory for Operators in Banach Spaces

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 2) Mon. 10:00 - 12:00 MZH 3150 Seminar

Additional dates:
Wed. 20.10.21 16:00 - 18:00 MZH 7200
PD Dr. Hendrik Vogt

Vertiefungsrichtung Numerik

Course numberTitle of eventLecturer
03-IMS-HROHighlights of Robust Optimization

Seminar (Teaching)
ECTS: 3 (auch als 6 möglich)

Additional dates:
Wed. 20.10.21 10:00 - 12:00 MZH 1100
Wed. 24.11.21 10:00 - 12:00 MZH 2490 (Seminarraum)

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.

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim
03-M-SEM-1Seminar zur Numerik partieller Differentialgleichungen
Numerical Methods for Partial Differential Equations

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 2340 Seminar


Alfred Schmidt
03-M-SEM-17High-Performance-Visualisierung
High-Performance Visualization
Ausgewählte Publikationen aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 6200 MZH 5210 Seminar

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.

Prof. Dr. Andreas Gerndt
03-M-SEM-26Diskrete Optimierung
Seminar und Proseminar

Seminar (Teaching)
ECTS: 6 (5)

Dates:
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Seminar/Proseminar

Additional dates:
Tue. 15.03.22 - Wed. 16.03.22 (Tue., Wed.) 08:00 - 18:00 MZH 1470

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.

Prof. Dr. Daniel Schmand
03-M-SEM-30Machine Learning in 3D and Mechanical Applications (in English)

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 4140 Seminar
David Erzmann

Vertiefungsrichtung Stochastik & Statistik

Course numberTitle of eventLecturer
03-M-SEM-28Informationstheorie (Seminar/ Proseminar)

Seminar (Teaching)
ECTS: 6(5)

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 1110 Seminar
Prof. Dr. Thorsten-Ingo Dickhaus

Master: Reading Courses

Course numberTitle of eventLecturer
03-M-RC-1Reading Course zur Algebra

Seminar (Teaching)
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-RC-2Reading Course zur Analysis

Seminar (Teaching)
ECTS: 9
Prof. Dr. Jens Rademacher
03-M-RC-3Reading Course zur Numerik

Seminar (Teaching)
ECTS: 9
Prof. Dr. Christof Büskens
03-M-RC-4Reading Course zur Stochastik/Statistik

Seminar (Teaching)
ECTS: 9

Additional dates:
Wed. 24.11.21 10:00 - 12:00 MZH 7200
Wed. 01.12.21 10:00 - 12:00 MZH 7200
Wed. 15.12.21 10:00 - 12:00 MZH 7200
Wed. 19.01.22 10:00 - 12:00 MZH 7200
Wed. 26.01.22 10:00 - 12:00 MZH 7200
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus

Oberseminare

Course numberTitle of eventLecturer
03-M-OS-2Oberseminar Angewandte Analysis

Seminar (Teaching)

Dates:
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 4140
Prof. Dr. Jens Rademacher
03-M-OS-3Oberseminar Angewandte Statistik

Seminar (Teaching)

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

Prof. Dr. Werner Brannath
03-M-OS-4Oberseminar Dynamische Systeme und Geometrie
Seminar: Dynamical Systems and Geometry

Seminar (Teaching)

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 4140

Weitere Infos auf der Seminar-Homepage

Prof. Dr. Marc Keßeböhmer
Prof. Dr. Anke Dorothea Pohl
03-M-OS-6Oberseminar: Deep Learning, Inverse Probleme und Datenanalyse
Research Seminar: Deep Learning, Inverse Problems and Data Analysis

Seminar (Teaching)

Dates:
weekly (starts in week: 1) Mon. 16:00 - 18:00 MZH 2340

Termin nach Vereinbarung.
Studiengang: T-M

Peter Maaß
Sören Dittmer
03-M-OS-7Oberseminar Mathematische Parameteridentifikation (in English)
Research Seminar - Mathematical Parameter Identification

Seminar (Teaching)

Dates:
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 2340


Daniel Otero Baguer
03-M-OS-9Oberseminar Optimierung & Optimale Steuerung
Seminar Optimisation and Optimal Control

Seminar (Teaching)


Prof. Dr. Christof Büskens
03-M-OS-11Oberseminar Algebra und Topologie

Seminar (Teaching)
Prof. Dr. Dmitry Feichtner-Kozlov
Prof. Dr. Eva-Maria Feichtner

Kolloquien

Course numberTitle of eventLecturer
03-M-KOL-1Mathematisches Kolloquium

Colloquium (Teaching)

Dates:
weekly (starts in week: 1) Tue. 16:00 - 18:00
Prof. Dr. Christine Knipping
Prof. Dr. Jens Rademacher

General Studies

Course numberTitle of eventLecturer
03-M-GS-2Modelle und Mathematik
Models and Math

Seminar (Teaching)
ECTS: 2

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 1110 Seminar
Ronald Stöver
03-M-GS-3Grundlegende Methoden der angewandten Statistik (in English)
Basic Methods of Applied Statistics

Lecture (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Fri. 08:00 - 10:00 SFG 0140 Vorlesung
weekly (starts in week: 1) Fri. 10:00 - 12:00 SFG 0140 Übung 1
weekly (starts in week: 1) Fri. 14:00 - 16:00 SFG 0140 Übung 2
Dr. Martin Scharpenberg
03-M-GS-5Statistische Beratung (in English)
Statistical Consulting

Seminar (Teaching)
ECTS: 3

Dates:
weekly (starts in week: 1) Fri. 10:00 - 12:00 Seminar
Dr. Martin Scharpenberg
03-M-GS-6Data Science in Natural Sciences using R (in English)
a course on R programming and data science methods with practicals and projects

Lecture (Teaching)
ECTS: 3

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 External location: MZH 0240 for Lecture and Work on Computers (2 Credit hours) Work on Computers in MZH-0240

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.

Prof. Dr. Stephan Frickenhaus
03-M-GS-7Einführung in R (in English)
Introduction to R

Seminar (Teaching)
ECTS: 3

Dates:
weekly (starts in week: 1) Fri. 13:00 - 16:00
Charlie Hillner
Prof. Dr. Werner Brannath