Veranstaltungsverzeichnis

Lehrveranstaltungen SoSe 2024

Mathematics, M.Sc.

Area of Specialization: Algebra

Modules: Specialization (A, B, and C with 9 CP each)

The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-15Analytic and Discrete Convex Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 4140 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
wöchentlich Mi 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

Modules: Diversification (A, B, and C with 9 CP each)

The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-Visualisierung (in englischer Sprache)
High-Performance Visualization
Interaktive Exploration zur Analyse von extrem großen wissenschaftlichen Daten

Vorlesung
ECTS: 4,5 / 6

Termine:
wöchentlich Do 14:00 - 16:00 MZH 1110 Lecture and Exercise

Einzeltermine:
Do 11.07.24 14:00 - 18:00 MZH 1110
Do 22.08.24 - Fr 23.08.24 (Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture

Einzeltermine:
Mi 10.07.24 14:00 - 16:00 ZOOM
Mi 17.07.24 14:00 - 16:00 ZOOM
Mo 22.07.24 - Fr 26.07.24 (Mo, Di, Mi, Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 12:00 - 14:00 Externer Ort: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

Prof. Dr. Christof Büskens
03-M-SP-22Linear and Generalized Linear Regression (Statistics II) (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Do 08:00 - 10:00 MZH 7200 Lecture

Einzeltermine:
Do 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-32Spectral Theory (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 4140 Lecture
wöchentlich Do 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Do 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-33Semiparametric Models (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 09:00 - 10:30 Vorlesung
wöchentlich Mi 14:00 - 16:00 Übung
wöchentlich Fr 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
wöchentlich Di 12:00 - 14:00 MZH 1470 Exercise
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Do 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Fr 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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

Compulsory module in the area of specialization in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-10Homologische Algebra

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-24Cohomology

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov

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

Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-21Deep Learning for Inverse Problems (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar

Einzeltermine:
Fr 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 englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Do 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 16:00 - 18:00 MZH 2340 Seminar

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

Module: Reading Course A (9 CP)

Compulsory module in the area of specialization and with the following course:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ALGReading Course Algebra (in englischer Sprache)

Seminar
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov

Module: Reading Course B (9 CP)

Compulsory module either in the area of specialization or area of diversification and with the following courses:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ALGReading Course Algebra (in englischer Sprache)

Seminar
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-RC-ANAReading Course Analysis (in englischer Sprache)

Seminar
ECTS: 9

Einzeltermine:
Do 13.06.24 08:00 - 10:00 MZH 4140

In the Reading Course 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-RC-NUMReading Course Numerical Analysis (in englischer Sprache)

Seminar
ECTS: 9

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

Analytical and structured thinking, exact formulation of mathematical facts, comprehension of mathematical proofs and learning of proof techniques, independent and creative solving of mathematical problems, knowledge of real analysis, algorithmic approach to solving mathematical problems.

Prof. Dr. Christof Büskens
03-M-RC-STSReading Course Statistics/Stochastics (in englischer Sprache)

Seminar
ECTS: 9

The reading course introduces students to specific topics that may be relevant for the Master's thesis, using mainly original English-language literature (scientific articles and reference books). Students are expected to prepare a seminar talk and an elaboration on the topic.

Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus

Area of Specialization: Analysis

Modules: Specialization (A, B, and C with 9 CP each)

The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-M-SP-16Mathematical Foundations of Machine Learning (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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-32Spectral Theory (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 4140 Lecture
wöchentlich Do 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Do 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-34Differential Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
wöchentlich Di 12:00 - 14:00 MZH 1470 Exercise
wöchentlich Mi 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

Modules: Diversification (A, B, and C with 9 CP each)

The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-Visualisierung (in englischer Sprache)
High-Performance Visualization
Interaktive Exploration zur Analyse von extrem großen wissenschaftlichen Daten

Vorlesung
ECTS: 4,5 / 6

Termine:
wöchentlich Do 14:00 - 16:00 MZH 1110 Lecture and Exercise

Einzeltermine:
Do 11.07.24 14:00 - 18:00 MZH 1110
Do 22.08.24 - Fr 23.08.24 (Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture

Einzeltermine:
Mi 10.07.24 14:00 - 16:00 ZOOM
Mi 17.07.24 14:00 - 16:00 ZOOM
Mo 22.07.24 - Fr 26.07.24 (Mo, Di, Mi, Do, Fr) 10:00 - 16:30

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

Alfred Schmidt
Prof. Dr. Stephan Frickenhaus
03-M-SP-15Analytic and Discrete Convex Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 4140 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 12:00 - 14:00 Externer Ort: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

Prof. Dr. Christof Büskens
03-M-SP-22Linear and Generalized Linear Regression (Statistics II) (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Do 08:00 - 10:00 MZH 7200 Lecture

Einzeltermine:
Do 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-33Semiparametric Models (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 09:00 - 10:30 Vorlesung
wöchentlich Mi 14:00 - 16:00 Übung
wöchentlich Fr 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-35Selected Topics in Convex Optimization (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Do 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Fr 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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

Compulsory module in the area of specialization and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-22Advanced Communication Analysis (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar

Einzeltermine:
Fr 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

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

Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-10Homologische Algebra

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-21Deep Learning for Inverse Problems (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-23Advanced Robust Control (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Do 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-24Cohomology

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 16:00 - 18:00 MZH 2340 Seminar

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

Module: Reading Course A (9 CP)

Compulsory module in the area of specialization and with the following course:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ANAReading Course Analysis (in englischer Sprache)

Seminar
ECTS: 9

Einzeltermine:
Do 13.06.24 08:00 - 10:00 MZH 4140

In the Reading Course 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

Module: Reading Course B (9 CP)

Compulsory module either in the area of specialization or area of diversification and with the following courses:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ALGReading Course Algebra (in englischer Sprache)

Seminar
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-RC-ANAReading Course Analysis (in englischer Sprache)

Seminar
ECTS: 9

Einzeltermine:
Do 13.06.24 08:00 - 10:00 MZH 4140

In the Reading Course 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-RC-NUMReading Course Numerical Analysis (in englischer Sprache)

Seminar
ECTS: 9

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

Analytical and structured thinking, exact formulation of mathematical facts, comprehension of mathematical proofs and learning of proof techniques, independent and creative solving of mathematical problems, knowledge of real analysis, algorithmic approach to solving mathematical problems.

Prof. Dr. Christof Büskens
03-M-RC-STSReading Course Statistics/Stochastics (in englischer Sprache)

Seminar
ECTS: 9

The reading course introduces students to specific topics that may be relevant for the Master's thesis, using mainly original English-language literature (scientific articles and reference books). Students are expected to prepare a seminar talk and an elaboration on the topic.

Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus

Area of Specialization: Numerical Analysis

Modules: Specialization (A, B, and C with 9 CP each)

The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-Visualisierung (in englischer Sprache)
High-Performance Visualization
Interaktive Exploration zur Analyse von extrem großen wissenschaftlichen Daten

Vorlesung
ECTS: 4,5 / 6

Termine:
wöchentlich Do 14:00 - 16:00 MZH 1110 Lecture and Exercise

Einzeltermine:
Do 11.07.24 14:00 - 18:00 MZH 1110
Do 22.08.24 - Fr 23.08.24 (Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture

Einzeltermine:
Mi 10.07.24 14:00 - 16:00 ZOOM
Mi 17.07.24 14:00 - 16:00 ZOOM
Mo 22.07.24 - Fr 26.07.24 (Mo, Di, Mi, Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 12:00 - 14:00 Externer Ort: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

Prof. Dr. Christof Büskens
03-M-SP-35Selected Topics in Convex Optimization (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Do 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Fr 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

Modules: Diversification (A, B, and C with 9 CP each)

The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-15Analytic and Discrete Convex Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 4140 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Do 08:00 - 10:00 MZH 7200 Lecture

Einzeltermine:
Do 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-32Spectral Theory (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 4140 Lecture
wöchentlich Do 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Do 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-33Semiparametric Models (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 09:00 - 10:30 Vorlesung
wöchentlich Mi 14:00 - 16:00 Übung
wöchentlich Fr 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
wöchentlich Di 12:00 - 14:00 MZH 1470 Exercise
wöchentlich Mi 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

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

Compulsory module in the area of specialization and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-21Deep Learning for Inverse Problems (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-23Advanced Robust Control (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Do 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 16:00 - 18:00 MZH 2340 Seminar

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

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

Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-10Homologische Algebra

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-22Advanced Communication Analysis (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar

Einzeltermine:
Fr 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-24Cohomology

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov

Module: Reading Course A (9 CP)

Compulsory module in the area of specialization and with the following course:
VAKTitel der VeranstaltungDozentIn
03-M-RC-NUMReading Course Numerical Analysis (in englischer Sprache)

Seminar
ECTS: 9

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

Analytical and structured thinking, exact formulation of mathematical facts, comprehension of mathematical proofs and learning of proof techniques, independent and creative solving of mathematical problems, knowledge of real analysis, algorithmic approach to solving mathematical problems.

Prof. Dr. Christof Büskens

Module: Reading Course B (9 CP)

Compulsory module either in the area of specialization or area of diversification and with the following courses:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ALGReading Course Algebra (in englischer Sprache)

Seminar
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-RC-ANAReading Course Analysis (in englischer Sprache)

Seminar
ECTS: 9

Einzeltermine:
Do 13.06.24 08:00 - 10:00 MZH 4140

In the Reading Course 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-RC-NUMReading Course Numerical Analysis (in englischer Sprache)

Seminar
ECTS: 9

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

Analytical and structured thinking, exact formulation of mathematical facts, comprehension of mathematical proofs and learning of proof techniques, independent and creative solving of mathematical problems, knowledge of real analysis, algorithmic approach to solving mathematical problems.

Prof. Dr. Christof Büskens
03-M-RC-STSReading Course Statistics/Stochastics (in englischer Sprache)

Seminar
ECTS: 9

The reading course introduces students to specific topics that may be relevant for the Master's thesis, using mainly original English-language literature (scientific articles and reference books). Students are expected to prepare a seminar talk and an elaboration on the topic.

Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus

Area of Specialization: Statistics/Stochastics

Modules: Specialization (A, B, and C with 9 CP each)

The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-M-SP-22Linear and Generalized Linear Regression (Statistics II) (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Do 08:00 - 10:00 MZH 7200 Lecture

Einzeltermine:
Do 25.07.24 10:00 - 12:00 MZH 7200
Maryam Movahedifar
03-M-SP-33Semiparametric Models (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 09:00 - 10:30 Vorlesung
wöchentlich Mi 14:00 - 16:00 Übung
wöchentlich Fr 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

Modules: Diversification (A, B, and C with 9 CP each)

The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 10:00 - 12:00 MZH 3150 Kurs
wöchentlich Do 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-Visualisierung (in englischer Sprache)
High-Performance Visualization
Interaktive Exploration zur Analyse von extrem großen wissenschaftlichen Daten

Vorlesung
ECTS: 4,5 / 6

Termine:
wöchentlich Do 14:00 - 16:00 MZH 1110 Lecture and Exercise

Einzeltermine:
Do 11.07.24 14:00 - 18:00 MZH 1110
Do 22.08.24 - Fr 23.08.24 (Do, Fr) 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture

Einzeltermine:
Mi 10.07.24 14:00 - 16:00 ZOOM
Mi 17.07.24 14:00 - 16:00 ZOOM
Mo 22.07.24 - Fr 26.07.24 (Mo, Di, Mi, Do, Fr) 10:00 - 16:30

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

Alfred Schmidt
Prof. Dr. Stephan Frickenhaus
03-M-SP-15Analytic and Discrete Convex Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 4140 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 1100 MZH 5220 (Didaktik-Labor) Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Di 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 10:00 - 12:00 Externer Ort: NEOS Gebäude 3. Etage Lecture
wöchentlich Do 12:00 - 14:00 Externer Ort: NEOS Gebäude 3. Etage Exercise

Die Veranstaltung findet im NEOS Gebäude statt.

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

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 4140 Lecture
wöchentlich Do 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Do 16:00 - 18:00 MZH 4140 Exercise
PD Dr. Hendrik Vogt
03-M-SP-34Differential Geometry (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Di 10:00 - 12:00 MZH 1110 MZH 4140 Lecture
wöchentlich Di 12:00 - 14:00 MZH 1470 Exercise
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Mi 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 englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Do 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Fr 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Exercise
Dirk Lorenz
Lionel Ngoupeyou Tondji

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

Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
VAKTitel der VeranstaltungDozentIn
03-M-AC-10Homologische Algebra

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-21Deep Learning for Inverse Problems (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Seminar
Peter Maaß
03-M-AC-22Advanced Communication Analysis (in englischer Sprache)

Seminar
ECTS: 4,5/6

Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar

Einzeltermine:
Fr 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 englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Do 10:00 - 12:00 MZH 2340 Seminar
Dr. Chathura Wanigasekara
03-M-AC-24Cohomology

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 10:00 - 12:00 Externer Ort: MZH 7200 Seminar
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-AC-28Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)

Seminar
ECTS: 4,5 / 6

Termine:
wöchentlich Mo 16:00 - 18:00 MZH 2340 Seminar

Seminar on advanced numerical methods for partial differential equations

Alfred Schmidt

Module: Reading Course A (9 CP)

Compulsory module in the area of specialization and with the following course:
VAKTitel der VeranstaltungDozentIn
03-M-RC-STSReading Course Statistics/Stochastics (in englischer Sprache)

Seminar
ECTS: 9

The reading course introduces students to specific topics that may be relevant for the Master's thesis, using mainly original English-language literature (scientific articles and reference books). Students are expected to prepare a seminar talk and an elaboration on the topic.

Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus

Module: Reading Course B (9 CP)

Compulsory module either in the area of specialization or area of diversification and with the following courses:
VAKTitel der VeranstaltungDozentIn
03-M-RC-ALGReading Course Algebra (in englischer Sprache)

Seminar
ECTS: 9
Prof. Dr. Dmitry Feichtner-Kozlov
03-M-RC-ANAReading Course Analysis (in englischer Sprache)

Seminar
ECTS: 9

Einzeltermine:
Do 13.06.24 08:00 - 10:00 MZH 4140

In the Reading Course 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-RC-NUMReading Course Numerical Analysis (in englischer Sprache)

Seminar
ECTS: 9

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

Analytical and structured thinking, exact formulation of mathematical facts, comprehension of mathematical proofs and learning of proof techniques, independent and creative solving of mathematical problems, knowledge of real analysis, algorithmic approach to solving mathematical problems.

Prof. Dr. Christof Büskens
03-M-RC-STSReading Course Statistics/Stochastics (in englischer Sprache)

Seminar
ECTS: 9

The reading course introduces students to specific topics that may be relevant for the Master's thesis, using mainly original English-language literature (scientific articles and reference books). Students are expected to prepare a seminar talk and an elaboration on the topic.

Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus