Course Catalog

Study Program SoSe 2024

Industrial Mathematics & Data Analysis, M.Sc.

Foundations (33 CP)

Module: Modeling Project (15 CP)

Compulsory module in which you must attend the following lecture this semester:
Course numberTitle of eventLecturer
03-M-MP-1Modeling Project (Part 1) (in English)

Seminar (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 2340 Seminar

Over the course of two semesters, the participants of the modeling project work in teams on a project in which they are supposed to use the mathematical knowledge they have already acquired in applications outside of mathematics. The project partners can be industrial companies or research institutes.

Tobias Kluth
Peter Maaß

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-21Deep Learning for Inverse Problems (in English)

Seminar (Teaching)
ECTS: 4,5/6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in English)

Seminar (Teaching)
ECTS: 4,5/6

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

Additional dates:
Fri. 24.05.24 12:00 - 16:00 MZH 1380/1400

In the Seminar Analysis advanced topics in the area of analysis are discussed. The precise topic for Summer semester 2024 will be decided upon with the participants

Prof. Dr. Anke Dorothea Pohl
03-M-AC-23Advanced Robust Control (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

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

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

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-M-SP-12High-Performance Visualization (in English)
Interactive Exploration for the Analysis of Large-scale Scientific Datasets

Lecture (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1110 Lecture and Exercise

Additional dates:
Thu. 11.07.24 14:00 - 18:00 MZH 1110
Thu. 22.08.24 - Fri. 23.08.24 (Thu., Fri.) 08:00 - 14:00 MZH 5500

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Prof. Dr. Andreas Gerndt
03-M-SP-15Analytic and Discrete Convex Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 4140 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 3150 Exercise

Convex Geometry plays an essential role in several branches of Mathematics, from Discrete Mathematics to Harmonic Analysis. We will discuss some major analytic and discrete aspects of convex geometry and some applications. Our topics will range from the boundary structure of convex bodies, particularly polytopes, to Hadwiger's volume characterisation, via geometric and functional inequalities, as well as geometric and analytic symmetrization techniques.

Eugenia Saorin Gomez
03-M-SP-16Mathematical Foundations of Machine Learning (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) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Exercise

This course deals with the mathematical foundations of machine learning with a strong focus on classical theory and well-established algorithms like, for example, support vector machines for classification, ridge regression, principal component analysis for dimensionality reduction and k-means clustering. In addition, deep learning with neural networks will be briefly discussed towards the end of the course.

Peter Maaß
Dr. Matthias Beckmann
03-M-SP-22Linear and Generalized Linear Regression (Statistics II) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 08:00 - 10:00 MZH 7200 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 7200 Exercise
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 7200 Lecture

Additional dates:
Thu. 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-33Semiparametric Models (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 09:00 - 10:30 Vorlesung
weekly (starts in week: 1) Wed. 14:00 - 16:00 Übung
weekly (starts in week: 1) Fri. 12:30 - 14:00 Vorlesung

Die Veranstaltung findet im KKSB statt.

The course will provide an introduction into the theory of semiparametric models. We will introduce and discuss the mathematical statistical theory for such models and will illustrate the utility of semiparametric models with a number of concrete models and data examples. Hence, the lecture and exercises will cover theoretical and practical aspects of semiparametric models. We will use the open statistical software R for the data applications.

Prof. Dr. Werner Brannath
03-M-SP-34Differential Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1470 Exercise
weekly (starts in week: 1) Wed. 16:00 - 18:00 MZH 4140 Lecture

This course will cover manifolds, vector bundles, embeddings and submersions, integral curves and flows, basics of Lie groups, differential forms and integration, Riemannian metrics, geodesics, connections, curvature, and further topics.

Prof. Dr. Anke Dorothea Pohl
03-M-SP-35Selected Topics in Convex Optimization (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 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 2490 (Seminarraum) Exercise

The course will discuss convex optimisation problems and present and analyse algorithms that solve them. Topics will include proximal splitting, acceleration, stochastic algorithms, and performance estimation.

Sebastian Banert
Dirk Lorenz
03-M-SP-36Selected Topics on Optimization for Machine Learning (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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-21Deep Learning for Inverse Problems (in English)

Seminar (Teaching)
ECTS: 4,5/6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in English)

Seminar (Teaching)
ECTS: 4,5/6

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

Additional dates:
Fri. 24.05.24 12:00 - 16:00 MZH 1380/1400

In the Seminar Analysis advanced topics in the area of analysis are discussed. The precise topic for Summer semester 2024 will be decided upon with the participants

Prof. Dr. Anke Dorothea Pohl
03-M-AC-23Advanced Robust Control (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

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

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

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-APX (03-ME-602.99a)Approximation Algorithms (in English)

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 2) Tue. 10:00 - 12:00 MZH 3150 Kurs
weekly (starts in week: 2) Thu. 14:00 - 16:00 MZH 1470 MZH 5500 Übung

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, IMVT-AI, IMVT-VMC
weitere Studiengänge: M-M-Alg-Num, M-T
https://lvb.informatik.uni-bremen.de/imat/03-imat-apx.pdf

A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be modeled as combinatorial optimization problems. In most cases, these problems are computationally intractable and one often resorts to heuristics that provide sufficiently good solutions in reasonable amount of runtime. However, in most cases, such heuristics do not provide a worst case guarantee on the performance in comparison to the optimum solution. In this course, we shall study algorithms for combinatorial optimization problems which can provide strong mathematical guarantees on performance. The course aims at developing a toolkit for solving such problems. The lectures will consist of designing polynomial-time algorithms and proving rigorous bounds on their worst case performances.
We review many classical results in the field of approximation algorithms, highlighting different techniques commonly used for the design of such algorithms. Among others, we will treat the following topics:
• Greedy algorithms and Local Search
• Rounding Data and Dynamic Programming
• Deterministic Rounding of Linear Programs (LPs)
• Random Sampling and Randomized Rounding of LPs
• Primal-Dual Methods
• Hardness of Approximation
• Problem Solving under Uncertainty

Dr. Felix Christian Hommelsheim
03-M-SP-12High-Performance Visualization (in English)
Interactive Exploration for the Analysis of Large-scale Scientific Datasets

Lecture (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1110 Lecture and Exercise

Additional dates:
Thu. 11.07.24 14:00 - 18:00 MZH 1110
Thu. 22.08.24 - Fri. 23.08.24 (Thu., Fri.) 08:00 - 14:00 MZH 5500

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Prof. Dr. Andreas Gerndt
03-M-SP-14Scientific Programming and Advanced Numerical Methods - an Introduction with Case Studies (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 2340 Lecture

Additional dates:
Wed. 10.07.24 14:00 - 16:00 ZOOM
Wed. 17.07.24 14:00 - 16:00 ZOOM
Mon. 22.07.24 - Fri. 26.07.24 (Mon., Tue., Wed., Thu., Fri.) 10:00 - 16:30

This course provides an introduction to the practice of scientific programming.

Alfred Schmidt
Prof. Dr. Stephan Frickenhaus
03-M-SP-16Mathematical Foundations of Machine Learning (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) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Exercise

This course deals with the mathematical foundations of machine learning with a strong focus on classical theory and well-established algorithms like, for example, support vector machines for classification, ridge regression, principal component analysis for dimensionality reduction and k-means clustering. In addition, deep learning with neural networks will be briefly discussed towards the end of the course.

Peter Maaß
Dr. Matthias Beckmann
03-M-SP-20Digital Optimal Control and Optimal Feedback Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 External location: NEOS Gebäude 3. Etage Lecture
weekly (starts in week: 1) Thu. 10:00 - 12:00 External location: NEOS Gebäude 3. Etage Lecture
weekly (starts in week: 1) Thu. 12:00 - 14:00 External location: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

Prof. Dr. Christof Büskens
03-M-SP-32Spectral Theory (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 4140 Lecture
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Thu. 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-34Differential Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1470 Exercise
weekly (starts in week: 1) Wed. 16:00 - 18:00 MZH 4140 Lecture

This course will cover manifolds, vector bundles, embeddings and submersions, integral curves and flows, basics of Lie groups, differential forms and integration, Riemannian metrics, geodesics, connections, curvature, and further topics.

Prof. Dr. Anke Dorothea Pohl
03-M-SP-35Selected Topics in Convex Optimization (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 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 2490 (Seminarraum) Exercise

The course will discuss convex optimisation problems and present and analyse algorithms that solve them. Topics will include proximal splitting, acceleration, stochastic algorithms, and performance estimation.

Sebastian Banert
Dirk Lorenz
03-M-SP-36Selected Topics on Optimization for Machine Learning (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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-21Deep Learning for Inverse Problems (in English)

Seminar (Teaching)
ECTS: 4,5/6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in English)

Seminar (Teaching)
ECTS: 4,5/6

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

Additional dates:
Fri. 24.05.24 12:00 - 16:00 MZH 1380/1400

In the Seminar Analysis advanced topics in the area of analysis are discussed. The precise topic for Summer semester 2024 will be decided upon with the participants

Prof. Dr. Anke Dorothea Pohl
03-M-AC-23Advanced Robust Control (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

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

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

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-APX (03-ME-602.99a)Approximation Algorithms (in English)

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 2) Tue. 10:00 - 12:00 MZH 3150 Kurs
weekly (starts in week: 2) Thu. 14:00 - 16:00 MZH 1470 MZH 5500 Übung

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, IMVT-AI, IMVT-VMC
weitere Studiengänge: M-M-Alg-Num, M-T
https://lvb.informatik.uni-bremen.de/imat/03-imat-apx.pdf

A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be modeled as combinatorial optimization problems. In most cases, these problems are computationally intractable and one often resorts to heuristics that provide sufficiently good solutions in reasonable amount of runtime. However, in most cases, such heuristics do not provide a worst case guarantee on the performance in comparison to the optimum solution. In this course, we shall study algorithms for combinatorial optimization problems which can provide strong mathematical guarantees on performance. The course aims at developing a toolkit for solving such problems. The lectures will consist of designing polynomial-time algorithms and proving rigorous bounds on their worst case performances.
We review many classical results in the field of approximation algorithms, highlighting different techniques commonly used for the design of such algorithms. Among others, we will treat the following topics:
• Greedy algorithms and Local Search
• Rounding Data and Dynamic Programming
• Deterministic Rounding of Linear Programs (LPs)
• Random Sampling and Randomized Rounding of LPs
• Primal-Dual Methods
• Hardness of Approximation
• Problem Solving under Uncertainty

Dr. Felix Christian Hommelsheim
03-M-SP-12High-Performance Visualization (in English)
Interactive Exploration for the Analysis of Large-scale Scientific Datasets

Lecture (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1110 Lecture and Exercise

Additional dates:
Thu. 11.07.24 14:00 - 18:00 MZH 1110
Thu. 22.08.24 - Fri. 23.08.24 (Thu., Fri.) 08:00 - 14:00 MZH 5500

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Prof. Dr. Andreas Gerndt
03-M-SP-14Scientific Programming and Advanced Numerical Methods - an Introduction with Case Studies (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Thu. 12:00 - 14:00 MZH 2340 Lecture

Additional dates:
Wed. 10.07.24 14:00 - 16:00 ZOOM
Wed. 17.07.24 14:00 - 16:00 ZOOM
Mon. 22.07.24 - Fri. 26.07.24 (Mon., Tue., Wed., Thu., Fri.) 10:00 - 16:30

This course provides an introduction to the practice of scientific programming.

Alfred Schmidt
Prof. Dr. Stephan Frickenhaus
03-M-SP-16Mathematical Foundations of Machine Learning (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) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Exercise

This course deals with the mathematical foundations of machine learning with a strong focus on classical theory and well-established algorithms like, for example, support vector machines for classification, ridge regression, principal component analysis for dimensionality reduction and k-means clustering. In addition, deep learning with neural networks will be briefly discussed towards the end of the course.

Peter Maaß
Dr. Matthias Beckmann
03-M-SP-20Digital Optimal Control and Optimal Feedback Control (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 External location: NEOS Gebäude 3. Etage Lecture
weekly (starts in week: 1) Thu. 10:00 - 12:00 External location: NEOS Gebäude 3. Etage Lecture
weekly (starts in week: 1) Thu. 12:00 - 14:00 External location: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

Prof. Dr. Christof Büskens
03-M-SP-32Spectral Theory (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 4140 Lecture
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 4140 Lecture
weekly (starts in week: 1) Thu. 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-34Differential Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1470 Exercise
weekly (starts in week: 1) Wed. 16:00 - 18:00 MZH 4140 Lecture

This course will cover manifolds, vector bundles, embeddings and submersions, integral curves and flows, basics of Lie groups, differential forms and integration, Riemannian metrics, geodesics, connections, curvature, and further topics.

Prof. Dr. Anke Dorothea Pohl
03-M-SP-35Selected Topics in Convex Optimization (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 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 2490 (Seminarraum) Exercise

The course will discuss convex optimisation problems and present and analyse algorithms that solve them. Topics will include proximal splitting, acceleration, stochastic algorithms, and performance estimation.

Sebastian Banert
Dirk Lorenz
03-M-SP-36Selected Topics on Optimization for Machine Learning (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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-21Deep Learning for Inverse Problems (in English)

Seminar (Teaching)
ECTS: 4,5/6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in English)

Seminar (Teaching)
ECTS: 4,5/6

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

Additional dates:
Fri. 24.05.24 12:00 - 16:00 MZH 1380/1400

In the Seminar Analysis advanced topics in the area of analysis are discussed. The precise topic for Summer semester 2024 will be decided upon with the participants

Prof. Dr. Anke Dorothea Pohl
03-M-AC-23Advanced Robust Control (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in English)

Seminar (Teaching)
ECTS: 4,5 / 6

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

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

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-M-SP-12High-Performance Visualization (in English)
Interactive Exploration for the Analysis of Large-scale Scientific Datasets

Lecture (Teaching)
ECTS: 4,5 / 6

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 1110 Lecture and Exercise

Additional dates:
Thu. 11.07.24 14:00 - 18:00 MZH 1110
Thu. 22.08.24 - Fri. 23.08.24 (Thu., Fri.) 08:00 - 14:00 MZH 5500

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Prof. Dr. Andreas Gerndt
03-M-SP-15Analytic and Discrete Convex Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 12:00 - 14:00 MZH 4140 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
weekly (starts in week: 1) Wed. 12:00 - 14:00 MZH 3150 Exercise

Convex Geometry plays an essential role in several branches of Mathematics, from Discrete Mathematics to Harmonic Analysis. We will discuss some major analytic and discrete aspects of convex geometry and some applications. Our topics will range from the boundary structure of convex bodies, particularly polytopes, to Hadwiger's volume characterisation, via geometric and functional inequalities, as well as geometric and analytic symmetrization techniques.

Eugenia Saorin Gomez
03-M-SP-16Mathematical Foundations of Machine Learning (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) Tue. 12:00 - 14:00 MZH 2340 Lecture
weekly (starts in week: 1) Tue. 14:00 - 16:00 MZH 2340 Exercise

This course deals with the mathematical foundations of machine learning with a strong focus on classical theory and well-established algorithms like, for example, support vector machines for classification, ridge regression, principal component analysis for dimensionality reduction and k-means clustering. In addition, deep learning with neural networks will be briefly discussed towards the end of the course.

Peter Maaß
Dr. Matthias Beckmann
03-M-SP-22Linear and Generalized Linear Regression (Statistics II) (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Wed. 08:00 - 10:00 MZH 7200 Lecture
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 7200 Exercise
weekly (starts in week: 1) Thu. 08:00 - 10:00 MZH 7200 Lecture

Additional dates:
Thu. 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-33Semiparametric Models (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 09:00 - 10:30 Vorlesung
weekly (starts in week: 1) Wed. 14:00 - 16:00 Übung
weekly (starts in week: 1) Fri. 12:30 - 14:00 Vorlesung

Die Veranstaltung findet im KKSB statt.

The course will provide an introduction into the theory of semiparametric models. We will introduce and discuss the mathematical statistical theory for such models and will illustrate the utility of semiparametric models with a number of concrete models and data examples. Hence, the lecture and exercises will cover theoretical and practical aspects of semiparametric models. We will use the open statistical software R for the data applications.

Prof. Dr. Werner Brannath
03-M-SP-34Differential Geometry (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Tue. 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
weekly (starts in week: 1) Tue. 12:00 - 14:00 MZH 1470 Exercise
weekly (starts in week: 1) Wed. 16:00 - 18:00 MZH 4140 Lecture

This course will cover manifolds, vector bundles, embeddings and submersions, integral curves and flows, basics of Lie groups, differential forms and integration, Riemannian metrics, geodesics, connections, curvature, and further topics.

Prof. Dr. Anke Dorothea Pohl
03-M-SP-35Selected Topics in Convex Optimization (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Mon. 10:00 - 12:00 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 2490 (Seminarraum) Exercise

The course will discuss convex optimisation problems and present and analyse algorithms that solve them. Topics will include proximal splitting, acceleration, stochastic algorithms, and performance estimation.

Sebastian Banert
Dirk Lorenz
03-M-SP-36Selected Topics on Optimization for Machine Learning (in English)

Lecture (Teaching)
ECTS: 9

Dates:
weekly (starts in week: 1) Thu. 14:00 - 16:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 10:00 - 12:00 MZH 2340 Lecture
weekly (starts in week: 1) Fri. 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji