Course Catalog

Study Program WiSe 2023/2024

Industrial Mathematics & Data Analysis, M.Sc.

Foundations (33 CP)

Module: Mathematical Methods for Data Analysis and Image Processing (9 CP)

Compulsory module in which you must attend the following lecture(s):
Course numberTitle of eventLecturer
03-M-MDAIP-1Mathematical Methods for Data Analysis and Image Processing (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 1090 Lecture
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 1380/1400 Lecture
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Exercise

Additional dates:
Mon. 05.02.24 10:00 - 14:00 MZH 5500
Dirk Lorenz

Module: Modeling Project (15 CP)

Compulsory module in which you must attend the following lecture this semester:
Course numberTitle of eventLecturer
03-M-MP-2Modelling Project (Part 2) (in English)

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 2340 Seminar
Prof. Dr. Andreas Rademacher

Module: Numerical Methods for Partial Differential Equations (9 CP)

Compulsory module in which you must attend the following lecture:
Course numberTitle of eventLecturer
03-M-NPDE-1Numerical Methods for Partial Differential Equations (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1450 Übung
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 2340 Lecture
Alfred Schmidt

Area of Focus: Data Analysis (45 CP)

Area of Focus (27 CP)

The modules Special Topics Data Analysis A and Special Topics Data Analysis B (9 CP each) are mandatory. In addition, EITHER the module Special Topics Data Analysis C OR the module Advanced Communications Data Analysis (9 CP each) must be studied.

Module: Advanced Communications Data Analysis (2 x 4,5 CP = 9 CP)

Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
Course numberTitle of eventLecturer
03-M-AC-2High-Performance-Visualisierung (in English)
High-Performance Visualization
Ausgewählte Publikationen aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1100 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-AC-5Mathematical Methods in Machine Learning (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 2340 Seminar
Peter Maaß
Sören Dittmer
03-M-AC-16Approximation Methods in Probability and Statistics (in English)

Seminar (Teaching)
ECTS: 3/ 4,5/ 5/ 6

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 7200 MZH 4140 Seminar
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-AC-19Convex Analysis (in English)

Seminar (Teaching)
ECTS: 3 / 4,5 / 5 / 6

Dates:
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 1090 Seminar

Additional dates:
Wed. 14.02.24 09:00 - 12:00
Dirk Lorenz

Modules: Special Topics Data Analysis (A, B, and C with 9 CP each)

Compulsory modules in which you must attend one lecture each. This semester you can choose from the following lectures:
Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6 (9)

Dates:
weekly (starts in week: 2) Mon. 10:00 - 12:00 MZH 1090 Kurs
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1470 Kurs

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf
First Lecture: Thursday, Sep 19.

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-SP-2Basics of Mathematical Statistics (Statistics I) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 MZH 1450 Lecture
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 1380/1400 Exercise
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 7200 Lecture

Additional dates:
Thu. 14.03.24 09:30 - 12:30 MZH 6200
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-SP-25Inverse Problems in Imaging (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 14:00 - 16:00 MZH 2340 Exercise
Peter Maaß
Dr. Matthias Beckmann
03-M-SP-26Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 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
03-M-SP-27Finite Elements for Contact Problems (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 2340 Exercise
Prof. Dr. Andreas Rademacher
03-M-SP-28Advanced Methods in Applied Statistics (Statistics III) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 08:00 - 10:00 Lecture
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 1470 Lecture
weekly (starts in week: 1) Fri. 08:00 - 10:00 MZH 2340 MZH 1380/1400 Exercise

Additional dates:
Fri. 12.01.24 08:00 - 10:00

Die Vorlesung am Di 8-10h findet im KKSB statt.

Prof. Dr. Werner Brannath
03-M-SP-30Introduction to Robust Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 2340 Exercise
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Lecture
Dr. Chathura Wanigasekara

Extension (18 CP)

Module: Advanced Communications Industrial Mathematics (2 x 4,5 CP = 9 CP)

Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
Course numberTitle of eventLecturer
03-M-AC-2High-Performance-Visualisierung (in English)
High-Performance Visualization
Ausgewählte Publikationen aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1100 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-AC-5Mathematical Methods in Machine Learning (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 2340 Seminar
Peter Maaß
Sören Dittmer
03-M-AC-19Convex Analysis (in English)

Seminar (Teaching)
ECTS: 3 / 4,5 / 5 / 6

Dates:
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 1090 Seminar

Additional dates:
Wed. 14.02.24 09:00 - 12:00
Dirk Lorenz
03-M-AC-20Numerical Methods and Neural Networks for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

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

Additional dates:
Wed. 06.03.24 13:00 - 16:00 MZH 2340
Mon. 11.03.24 10:00 - 13:00 MZH 2340
Alfred Schmidt
Prof. Dr. Andreas Rademacher

Module: Special Topics Industrial Mathematics A (9 CP)

Compulsory module in which you must attend one lecture. This semester you can choose from the following lectures:
Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6 (9)

Dates:
weekly (starts in week: 2) Mon. 10:00 - 12:00 MZH 1090 Kurs
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1470 Kurs

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf
First Lecture: Thursday, Sep 19.

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-SP-2Basics of Mathematical Statistics (Statistics I) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 MZH 1450 Lecture
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 1380/1400 Exercise
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 7200 Lecture

Additional dates:
Thu. 14.03.24 09:30 - 12:30 MZH 6200
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-SP-25Inverse Problems in Imaging (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 14:00 - 16:00 MZH 2340 Exercise
Peter Maaß
Dr. Matthias Beckmann
03-M-SP-26Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 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
03-M-SP-27Finite Elements for Contact Problems (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 2340 Exercise
Prof. Dr. Andreas Rademacher
03-M-SP-30Introduction to Robust Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 2340 Exercise
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Lecture
Dr. Chathura Wanigasekara
03-M-SP-31Direct Methods for Optimal Feedback Control (in English)
Introduction to Nonlinear Optimization, Optimal Control and Optimal Feedback Control

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 Lecture
weekly (starts in week: 1) Wed. 08:00 - 10:00 Exercise
weekly (starts in week: 1) Thu. 08:00 - 10:00 Lecture

Die Veranstaltung findet im NEOS Gebäude Raum 3410 statt.

Prof. Dr. Christof Büskens
04-M30-CP-SFT-3Trajectory Optimization (in English)

Lecture (Teaching)
ECTS: 4,5

Dates:
weekly (starts in week: 1) Mon. 14:00 - 18:00 FZB 0240

Additional dates:
Thu. 29.02.24 14:00 - 16:00 SFG 0150
Prof. Dr. Christof Büskens
Matthias Knauer

Area of Focus: Industrial Mathematics (45 CP)

Area of Focus (27 CP)

The modules Special Topics Industrial Mathematics A and Special Topics Industrial Mathematics B (9 CP each) are mandatory. In addition, EITHER the module Special Topics Industrial Mathematics C OR the module Advanced Communications Industrial Mathematics (9 CP each) must be studied.

Module: Advanced Communications Industrial Mathematics (2 x 4,5 CP = 9 CP)

Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
Course numberTitle of eventLecturer
03-M-AC-2High-Performance-Visualisierung (in English)
High-Performance Visualization
Ausgewählte Publikationen aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1100 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-AC-5Mathematical Methods in Machine Learning (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 2340 Seminar
Peter Maaß
Sören Dittmer
03-M-AC-19Convex Analysis (in English)

Seminar (Teaching)
ECTS: 3 / 4,5 / 5 / 6

Dates:
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 1090 Seminar

Additional dates:
Wed. 14.02.24 09:00 - 12:00
Dirk Lorenz
03-M-AC-20Numerical Methods and Neural Networks for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

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

Additional dates:
Wed. 06.03.24 13:00 - 16:00 MZH 2340
Mon. 11.03.24 10:00 - 13:00 MZH 2340
Alfred Schmidt
Prof. Dr. Andreas Rademacher

Modules: Special Topics Industrial Mathematics (A, B, and C with 9 CP each)

Compulsory modules in which you must attend one lecture each. This semester you can choose from the following lectures:
Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6 (9)

Dates:
weekly (starts in week: 2) Mon. 10:00 - 12:00 MZH 1090 Kurs
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1470 Kurs

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf
First Lecture: Thursday, Sep 19.

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-SP-2Basics of Mathematical Statistics (Statistics I) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 MZH 1450 Lecture
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 1380/1400 Exercise
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 7200 Lecture

Additional dates:
Thu. 14.03.24 09:30 - 12:30 MZH 6200
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-SP-25Inverse Problems in Imaging (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 14:00 - 16:00 MZH 2340 Exercise
Peter Maaß
Dr. Matthias Beckmann
03-M-SP-26Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 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
03-M-SP-27Finite Elements for Contact Problems (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 2340 Exercise
Prof. Dr. Andreas Rademacher
03-M-SP-30Introduction to Robust Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 2340 Exercise
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Lecture
Dr. Chathura Wanigasekara
03-M-SP-31Direct Methods for Optimal Feedback Control (in English)
Introduction to Nonlinear Optimization, Optimal Control and Optimal Feedback Control

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 Lecture
weekly (starts in week: 1) Wed. 08:00 - 10:00 Exercise
weekly (starts in week: 1) Thu. 08:00 - 10:00 Lecture

Die Veranstaltung findet im NEOS Gebäude Raum 3410 statt.

Prof. Dr. Christof Büskens
04-M30-CP-SFT-3Trajectory Optimization (in English)

Lecture (Teaching)
ECTS: 4,5

Dates:
weekly (starts in week: 1) Mon. 14:00 - 18:00 FZB 0240

Additional dates:
Thu. 29.02.24 14:00 - 16:00 SFG 0150
Prof. Dr. Christof Büskens
Matthias Knauer

Extension (18 CP)

Module: Advanced Communications Data Analysis (2 x 4,5 CP = 9 CP)

Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
Course numberTitle of eventLecturer
03-M-AC-2High-Performance-Visualisierung (in English)
High-Performance Visualization
Ausgewählte Publikationen aus dem Bereich der Visualisierung großer wissenschaftlicher Datensätze

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1100 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-AC-5Mathematical Methods in Machine Learning (in English)

Seminar (Teaching)
ECTS: 4,5/ 6

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 2340 Seminar
Peter Maaß
Sören Dittmer
03-M-AC-16Approximation Methods in Probability and Statistics (in English)

Seminar (Teaching)
ECTS: 3/ 4,5/ 5/ 6

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 7200 MZH 4140 Seminar
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-AC-19Convex Analysis (in English)

Seminar (Teaching)
ECTS: 3 / 4,5 / 5 / 6

Dates:
weekly (starts in week: 1) Tue. 16:00 - 18:00 MZH 1090 Seminar

Additional dates:
Wed. 14.02.24 09:00 - 12:00
Dirk Lorenz

Module: Special Topics Data Analysis A (9 CP)

Compulsory module in which you must attend one lecture. This semester you can choose from the following lectures:
Course numberTitle of eventLecturer
03-IMAT-AU (03-ME-602.99c)Algorithms and Uncertainty (in English)

Kurs (Teaching)
ECTS: 6 (9)

Dates:
weekly (starts in week: 2) Mon. 10:00 - 12:00 MZH 1090 Kurs
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1470 Kurs

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf
First Lecture: Thursday, Sep 19.

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-SP-2Basics of Mathematical Statistics (Statistics I) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 7200 MZH 1450 Lecture
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 1380/1400 Exercise
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 7200 Lecture

Additional dates:
Thu. 14.03.24 09:30 - 12:30 MZH 6200
Prof. Dr. Thorsten-Ingo Dickhaus
03-M-SP-25Inverse Problems in Imaging (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 14:00 - 16:00 MZH 2340 Exercise
Peter Maaß
Dr. Matthias Beckmann
03-M-SP-26Algebraische Topologie
Algebraic Topology

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 08:00 - 10:00 MZH 7200 MZH 4140 Vorlesung
weekly (starts in week: 1) Tue. 14:00 - 16:00 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
03-M-SP-27Finite Elements for Contact Problems (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 2340 Exercise
Prof. Dr. Andreas Rademacher
03-M-SP-28Advanced Methods in Applied Statistics (Statistics III) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 08:00 - 10:00 Lecture
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 1470 Lecture
weekly (starts in week: 1) Fri. 08:00 - 10:00 MZH 2340 MZH 1380/1400 Exercise

Additional dates:
Fri. 12.01.24 08:00 - 10:00

Die Vorlesung am Di 8-10h findet im KKSB statt.

Prof. Dr. Werner Brannath
03-M-SP-30Introduction to Robust Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 08:00 - 10:00 MZH 2340 Exercise
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 4140 Lecture
Dr. Chathura Wanigasekara