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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Independent study of an advanced selected topic in the area of analysis; can serve as a basis for a Master's thesis.
Please sign in on Stud.IP for the Reading Course in order to obtain up-to-date information on the course.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Das Seminar findet im Neos Gebäude statt. Raum nach Absprache.
Students study special topics of numerical mathematics in this reading course. The aim is a self-study of selected topics on the basis of textbooks, scientific articles or other monographs.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

Profil: SQ.
Schwerpunkt: IMVP-DMI, IMVP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-dis.pdf
Die Veranstaltung ist inhaltlich identisch mit "Entwurf von Informationssystemen" (keine Doppelanerkennung möglich).

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mpar.pdf
Profil: SQ, KIKR, DMI.
Schwerpunkt: SQ, AI, DMI, VMC
Some prior expertise in C will be helpful. The lecture will be held in German or English, depending on demand.
https://cgvr.cs.uni-bremen.de/teaching/

Schwerpunkt: IMAP-VMC, IMVP-DMI. IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-mia.pdf

Profil: KIKR, DMI
Schwerpunkt: IMA-AI, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-uuw.pdf

Schwerpunkt: IMVA-AI, IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-acss.pdf
The aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.

The lectures will explain the necessary fundamentals, such as sensor technology, user feedback, and the conduction of empirical studies, along with a number of inspiring examples.

In the project part, own prototypes for sports applications are developed in small groups. The exact application as well as the technical implementation approach can be chosen freely. The final graded outcome of the course will be a small sports application about which a presentation has to be held and a documentation in a scientific paper style has to be written.

The course will be held in English.

Profil: DMI.
Schwerpunkt: IMVA-DMI, IMVA-VMC
https://lvb.informatik.uni-bremen.de/imva/03-imva-ei.pdf

https://lvb.informatik.uni-bremen.de/imva/03-imva-ec2.pdf

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

Schwerpunkt: IMAP-VMC, IMVP-DMI. IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-mia.pdf

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-qse.pdf

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

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-llml.pdf

Profil: KIKR.
Schwerpunkt: IMA-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rl.pdf
Die Veranstaltung findet im DFKI statt.

Profil: KIKR, DMI
Schwerpunkt: IMA-AI, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-uuw.pdf

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR
Schwerpunkt: IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-wawr.pdf
Die Veranstaltung findet im TAB statt.

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-llml.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mpar.pdf
Profil: SQ, KIKR, DMI.
Schwerpunkt: SQ, AI, DMI, VMC
Some prior expertise in C will be helpful. The lecture will be held in German or English, depending on demand.
https://cgvr.cs.uni-bremen.de/teaching/

Schwerpunkt: IMAP-VMC, IMVP-DMI. IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-mia.pdf

Profil: KIKR.
Schwerpunkt: IMA-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rl.pdf
Die Veranstaltung findet im DFKI statt.

IMVP-AI
Room: TAB Knowledge (0.30)

Learning Outcome:
* Understand and apply concepts of functional programming
* Understand and apply artificial intelligence techniques
* Program an autonomous robot platform using ROS
* Implement failure handling techniques

Contents:
This course gives a solid practical introduction to the Robot Operating System (ROS2) up to advanced topics on how to control robots.
The lecture covers the tools of ROS2 and the cognitive robot architecture CRAM.
Students work in small groups on a TurtleBot to apply the lectures content hands-on to the robot.
Eventually the students will be able to design an autonomously driving vehicle, being evaluated in a final competition.

Profile: KIKR
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-rpros.pdf

https://lvb.informatik.uni-bremen.de/imap/03-imap-secoro.pdf

Profil: KIKR, DMI
Schwerpunkt: IMA-AI, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-uuw.pdf

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

Schwerpunkt: IMVA-AI, IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-acss.pdf
The aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.

The lectures will explain the necessary fundamentals, such as sensor technology, user feedback, and the conduction of empirical studies, along with a number of inspiring examples.

In the project part, own prototypes for sports applications are developed in small groups. The exact application as well as the technical implementation approach can be chosen freely. The final graded outcome of the course will be a small sports application about which a presentation has to be held and a documentation in a scientific paper style has to be written.

The course will be held in English.

Profil: KIKR
Schwerpunkt: IMVA-AI
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ims.pdf
Die Vorlesung „Einführung in Intelligent Marine Systeme“ setzt sich aus drei Hauptelementen zusammen:
1. Vermittlung der Grundlagen die beim Entwurf und der Entwicklung mariner Systeme, vornehmlich Unterwasser-Systeme, zu berücksichtigen sind. Dazu gehören neben der Vorstellung der verschiedenen Systemkonzepte wie z.B. Remotely Operated Vehicles (ROV) und Autonomous Underwater Vehicles (AUV) und der Sensorik auch Methoden der Navigation, Kommunikation, Antrieb, Energieversorgung und Steuerung.
2. Gastvorträge von Entwicklern, Anwendern und potenzieller Nutzer mit Besuch des des MARUM.

3. Ein, mit den TeilnehmerInnen, zusammen entwickeltes und ausgearbeitetes Systemkonzept, welches auf die verschiedenen meerestechnisch spezifischen Gesichtspunkte (siehe 1.) eingeht. (Präsentation, Peer-evaluation)

Ziele der Vorlesung:
• Grundlegendes Verständnis der marinen Umwelt im Kontext technischer Systeme
• Verständnis der spezifischen Herausforderungen mariner Systeme gegenüber terrestrischen Systemlösungen
• Übersicht über den gegenwärtigen Stand der Technik bei mobilen und stationären Systemen
• Übersicht der verschiedenen Sensormodalitäten, die gegenwärtig eingesetzt werden
• Fähigkeit ein einfaches Systemkonzeptunter Berücksichtigung der maritimen Randbedingungen zusammenzustellen.

Prüfungsform:
Individuelles Fachgespräch oder mündliche Prüfung
Peer evaluierte Präsentation (ausgeführt in Kleingruppen: 2-3 Personen)

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

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-qse.pdf

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mpar.pdf
Profil: SQ, KIKR, DMI.
Schwerpunkt: SQ, AI, DMI, VMC
Some prior expertise in C will be helpful. The lecture will be held in German or English, depending on demand.
https://cgvr.cs.uni-bremen.de/teaching/

Schwerpunkt: IMAP-VMC, IMVP-DMI. IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-mia.pdf

Profil: KIKR.
Schwerpunkt: IMA-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rl.pdf
Die Veranstaltung findet im DFKI statt.

Profil: KIKR, DMI
Schwerpunkt: IMA-AI, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-uuw.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-aca.pdf

Profil: SQ.
Schwerpunkt: IMVP-DMI, IMVP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-dis.pdf
Die Veranstaltung ist inhaltlich identisch mit "Entwurf von Informationssystemen" (keine Doppelanerkennung möglich).

Profil: SQ
Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-dls.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mpar.pdf
Profil: SQ, KIKR, DMI.
Schwerpunkt: SQ, AI, DMI, VMC
Some prior expertise in C will be helpful. The lecture will be held in German or English, depending on demand.
https://cgvr.cs.uni-bremen.de/teaching/

Profil: SQ.
Schwerpunkt: IMAP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-qse.pdf

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

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

Profil: DMI.
Schwerpunkt: IMVA-DMI, IMVA-VMC
https://lvb.informatik.uni-bremen.de/imva/03-imva-ei.pdf

https://lvb.informatik.uni-bremen.de/imva/03-imva-ec2.pdf

Profil: KIKR, DMI, MC.
Schwerpunkt: IMAP-VMC, IMAP-DMI, IMVP-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-acg.pdf
The lecture will be held in German or English, depending on demand. Most of the homework and assignments of this course will be practical ones. Decent programming skills in C++ are required to work on those.
https://cgvr.cs.uni-bremen.de/teaching/

http://www.uni-bremen.de/marstones/lehre/masterstudium

Registration for this lab must be done via Stud.IP.
Participants are selected on the basis of the exam passed in Control Theory I.
Please remember that this lab is in English. The preparation tasks therefore also have to be answered in English. Answers in German can not be accepted.
Registration period until 28.03.2025

Registration in the groups can then take place from 29.03.2025 to 14.04.2025.

If there are questions, please contact A. Niaz (0421 218 62727).
-
Anmeldung ausschließlich über Stud.IP..
Die Auswahl der Teilnehmer erfolgt auf der Grundlage der in der Kontrolltheorie I bestandene Klausur.
Bitte denken Sie daran, dass dieses Labor in Englisch ist. Die Vorbereitungsaufgaben müssen daher auch auf Englisch beantwortet werden. Antworten auf Deutsch können nicht akzeptiert werden.
Anmeldezeitraum bis zum 28.03.2025

Ab dem 29.03.2025 bis zum 14.04.2025 können dann die Eintragung in den Gruppen stattfinden.

Bei Fragen kontaktieren Sie bitte A. Niaz (0421 218 62727).



Termine nach Vereinbarung.

The ICT lab consists of multiple parts, which are organized by three departments: the RF department, ComNets and the dept. of communications engineering. Please refer to the individual departments for further information:

http://www.hf.uni-bremen.de/
http://www.comnets.uni-bremen.de/
http://www.ant.uni-bremen.de/courses/ictlab/

Registration for this lab must be done via Stud.IP.
Participants are selected on the basis of the exam passed in Control Theory I.
Please remember that this lab is in English. The preparation tasks therefore also have to be answered in English. Answers in German can not be accepted.
Registration period until 28.03.2025

Registration in the groups can then take place from 29.03.2025 to 14.04.2025.

If there are questions, please contact A. Niaz (0421 218 62727).
-
Anmeldung ausschließlich über Stud.IP..
Die Auswahl der Teilnehmer erfolgt auf der Grundlage der in der Kontrolltheorie I bestandene Klausur.
Bitte denken Sie daran, dass dieses Labor in Englisch ist. Die Vorbereitungsaufgaben müssen daher auch auf Englisch beantwortet werden. Antworten auf Deutsch können nicht akzeptiert werden.
Anmeldezeitraum bis zum 28.03.2025

Ab dem 29.03.2025 bis zum 14.04.2025 können dann die Eintragung in den Gruppen stattfinden.

Bei Fragen kontaktieren Sie bitte A. Niaz (0421 218 62727).



Termine nach Vereinbarung.

In the first 3 weeks, the lectures will be in room H2. All further weekly appointments: Rooms depending on the respective experiment. / (4 SWS)
Please check the PEP homepage for further course details (lecture + practical).

In the first 3 weeks, the lectures will be in room H2. All further weekly appointments: Rooms depending on the respective experiment. / (2 SWS) / Please check the PEP homepage for further course details (lecture + practicals).

Kenntnisse über die Herstellung keramischer Bauteile. Themen sind die Entwicklung keramischer Bauteile, Pulververarbeitung und -behandlung, Formgebung von Grünlingen, Sintern, mikrostrukturelle Entwicklung während der Verarbeitung keramischer Werkstoffe, Endbearbeitung wie Schleifen oder Beschichten.
Inhalt:
1. Einführung;
2. Keramische Pulver;
3. Aufbereitung keramischer Massen;
4. Formgebung 1 – Presstechniken;
5. Formgebung 2 – Schlickergusstechniken;
6. Formgebung 3 – Plastische Formungsverfahren;
7. Poröse Keramik;
8. Sintern und Mikrostrukturcharakterisierung;
9. Sintern – Fortgeschrittene Techniken;
10. Beispiele von Hochleistungskeramik;
11. Zusammenfassung.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Statistical problems are described by statistical models. This means interpreting the data as realizations of random variables whose unconditional or conditional densities are described and estimated by statistical (regression) models. These models are usually identified by a set of parameters, which can be finite but also infinite dimensional. For this purpose, there are usually three types of possible models, depending on the structure of the data and the problem at hand: parametric, nonparametric, and semiparametric. A semiparametric model is characterized by the inclusion of both finite dimensional parametric and infinite dimensional nonparametric components. The main interest is usually in the finite dimensional parametric component, with the infinite dimensional component being co-estimated for the purpose of statistical inference and efficiency. In this seminar we will study the definition, properties, and applications of semiparametric models. Examples of semiparametric models include single-index models and Cox regression models for censored survival time data. We will also consider approaches to dealing with missing information in data sets. The use of semiparametric models plays a major role for medical studies, for example.

Prerequisites for participation in the seminar are basic knowledge of mathematical statistics (e.g. from Statistics 1) and of regression models (e.g. from Statistics 2). English speaking students are welcome.

In order to gain a good insight into the extensive theory of semi-parametric models, we will use the master thesis by Karel Vermeulen as our primary literature and study the chapters that are important for us in more detail, presenting the knowledge gained through the thesis in the form of individual presentations.

A list with the name of the thesis and further literature can be found below.

• Master thesis of Karel Vermeulen. Semiparametric Efficiency
• A. W. van der Vaart. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
• A. W. van der Vaart. ”On Differentiable Functionals“. In: Ann. Statist. 19.1 (März 1991), S. 178–204.
• Tsiatis, Anastasios. Semiparametric Theory and Missing Data. Vereinigtes Königreich, Springer New York, 2010.

Independent study of an advanced selected topic in the area of analysis; can serve as a basis for a Master's thesis.
Please sign in on Stud.IP for the Reading Course in order to obtain up-to-date information on the course.

Independent study of an advanced selected topic in the area of analysis; can serve as a basis for a Master's thesis.
Please sign in on Stud.IP for the Reading Course in order to obtain up-to-date information on the course.

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html

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

Die Veranstaltung findet im NEOS Gebäude statt.

The lecture addresses Interactive Visualization of huge scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-hpvis

In the focus of this lecture are modern numerical tools for modeling applications and the corresponding research software. In the lecture, modern numerical methods and their demands on implementations are given. In the seminar exercises, principles of numerical software design are introduced and implementations are tested interactively. As third element, in a project week students focus on the practical implementation for a selected application case.

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

This course in commutative algebra covers the theory of ideals in polynomial rings, affine algebraic varieties, Gröbner bases, and the Buchberger algorithm, providing a basic introduction to algebraic geometry. It also develops concepts from homological algebra, including the theory of R-modules, injective and projective modules, chain complexes, homology, presentations and free resolutions, multigraded modules, and the decomposition theorem of R-modules.

We will discuss linear and generalized linear regression models. While the models studied in Statistics I are mainly models for stochastically independent and identically distributed observables, regression models are used to model stochastically independent, but not identically distributed observables. In addition to the theoretical investigations, we will discuss real-life applications and software solutions (in R).

In this lecture we will concentrate on 3 basic types of partial differential equations: the Poisson equation, the wave equation and the heat equation – which are prototypes of elliptic, hyperbolic and parabolic differential equations. They are linear equations, and for the solution theory we will make some use of linear functional analysis. See also http://www.math.uni-bremen.de/~hvogt/pde26.html