https://lvb.informatik.uni-bremen.de/ibat/03-ibat-alg.pdf
Algorithms are a fundamental part of computer science. An algorithm is an abstract description of a procedure for solving a problem. Understanding how to design efficient algorithms is an essential skill for developing complex programs, models, and applications.

This course assumes basic knowledge of algorithm design principles and algorithm analysis. Building on these foundatinos, we explore faster and more sophisticated algorithms for well-known problems such as

• network flows, and
• maximum matchings in bipartite graphs.

Beyond these, we study more general problems and develop algorithms to solve them, including:

• minimum-cost flows, 
• maximum matchings in general graphs, and
• stable matchings.

Additionally, we introduce new concepts that model a broad class of fundamental problems and explore fast meta-algorithms for them. These topics include:

• linear programming and the ellipsoid method, and
• matroids, the Greedy algorithm, and matroid intersection.

The goals of this course are to provide a broad overview of fundamental problems in algorithmics and combinatorial optimization. Moreover, participants will develop a strong toolkit for designing and analyzing efficient algorithms, well beyond the standard undergraduate level in algorithm theory.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf

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.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-alg.pdf
Algorithms are a fundamental part of computer science. An algorithm is an abstract description of a procedure for solving a problem. Understanding how to design efficient algorithms is an essential skill for developing complex programs, models, and applications.

This course assumes basic knowledge of algorithm design principles and algorithm analysis. Building on these foundatinos, we explore faster and more sophisticated algorithms for well-known problems such as

• network flows, and
• maximum matchings in bipartite graphs.

Beyond these, we study more general problems and develop algorithms to solve them, including:

• minimum-cost flows, 
• maximum matchings in general graphs, and
• stable matchings.

Additionally, we introduce new concepts that model a broad class of fundamental problems and explore fast meta-algorithms for them. These topics include:

• linear programming and the ellipsoid method, and
• matroids, the Greedy algorithm, and matroid intersection.

The goals of this course are to provide a broad overview of fundamental problems in algorithmics and combinatorial optimization. Moreover, participants will develop a strong toolkit for designing and analyzing efficient algorithms, well beyond the standard undergraduate level in algorithm theory.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf

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.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-alg.pdf
Algorithms are a fundamental part of computer science. An algorithm is an abstract description of a procedure for solving a problem. Understanding how to design efficient algorithms is an essential skill for developing complex programs, models, and applications.

This course assumes basic knowledge of algorithm design principles and algorithm analysis. Building on these foundatinos, we explore faster and more sophisticated algorithms for well-known problems such as

• network flows, and
• maximum matchings in bipartite graphs.

Beyond these, we study more general problems and develop algorithms to solve them, including:

• minimum-cost flows, 
• maximum matchings in general graphs, and
• stable matchings.

Additionally, we introduce new concepts that model a broad class of fundamental problems and explore fast meta-algorithms for them. These topics include:

• linear programming and the ellipsoid method, and
• matroids, the Greedy algorithm, and matroid intersection.

The goals of this course are to provide a broad overview of fundamental problems in algorithmics and combinatorial optimization. Moreover, participants will develop a strong toolkit for designing and analyzing efficient algorithms, well beyond the standard undergraduate level in algorithm theory.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf

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.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-alg.pdf
Algorithms are a fundamental part of computer science. An algorithm is an abstract description of a procedure for solving a problem. Understanding how to design efficient algorithms is an essential skill for developing complex programs, models, and applications.

This course assumes basic knowledge of algorithm design principles and algorithm analysis. Building on these foundatinos, we explore faster and more sophisticated algorithms for well-known problems such as

• network flows, and
• maximum matchings in bipartite graphs.

Beyond these, we study more general problems and develop algorithms to solve them, including:

• minimum-cost flows, 
• maximum matchings in general graphs, and
• stable matchings.

Additionally, we introduce new concepts that model a broad class of fundamental problems and explore fast meta-algorithms for them. These topics include:

• linear programming and the ellipsoid method, and
• matroids, the Greedy algorithm, and matroid intersection.

The goals of this course are to provide a broad overview of fundamental problems in algorithmics and combinatorial optimization. Moreover, participants will develop a strong toolkit for designing and analyzing efficient algorithms, well beyond the standard undergraduate level in algorithm theory.

Die Veranstaltung findet im TAB in Raum 0.30 statt.
https://lvb.informatik.uni-bremen.de/ibv/03-ibvp-akr.pdf

This course deals with the idea of bringing knowledge into applications to support users in daily life. It therefore covers topics on how knowledge can be represented to be machine-understandable, how knowledge can be acquired from different sources (including Web scraping) and how such different knowledge chunks can be linked. It will further discuss how to reason about knowledge and how different agents like websites, AR applications or robots can use knowledge to support users in their daily life. All exercises will be available in platform-independent jupyter notebooks based on python and have low software requirements.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf

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.

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2025 sowie im Wintersemester 2025/26 anerkannt.
Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2025 as well as in winter semester 2025/26.
Further information, schedules and registration only on the website https://www.informatica-feminale.de.

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2025 sowie im Wintersemester 2025/26 anerkannt.
Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2025 as well as in winter semester 2025/26.
Further information, schedules and registration only on the website https://www.informatica-feminale.de.

4 SWS, Profil: DMI
Schwerpunkt: IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-3dmft.pdf
Kurzbeschreibung unter: http://dimeb.informatik.uni-bremen.de/FabLab/GenerativeDesign.pdf
Präsenz-Meeting mittwochs 10-12h und anschließend 2 Std Übungen im FabLab wöchentlich.

Die Veranstaltung findet im TAB in Raum 0.30 statt.
https://lvb.informatik.uni-bremen.de/ibv/03-ibvp-akr.pdf

This course deals with the idea of bringing knowledge into applications to support users in daily life. It therefore covers topics on how knowledge can be represented to be machine-understandable, how knowledge can be acquired from different sources (including Web scraping) and how such different knowledge chunks can be linked. It will further discuss how to reason about knowledge and how different agents like websites, AR applications or robots can use knowledge to support users in their daily life. All exercises will be available in platform-independent jupyter notebooks based on python and have low software requirements.

https://lvb.informatik.uni-bremen.de/ibat/03-ibat-alg.pdf
Algorithms are a fundamental part of computer science. An algorithm is an abstract description of a procedure for solving a problem. Understanding how to design efficient algorithms is an essential skill for developing complex programs, models, and applications.

This course assumes basic knowledge of algorithm design principles and algorithm analysis. Building on these foundatinos, we explore faster and more sophisticated algorithms for well-known problems such as

• network flows, and
• maximum matchings in bipartite graphs.

Beyond these, we study more general problems and develop algorithms to solve them, including:

• minimum-cost flows, 
• maximum matchings in general graphs, and
• stable matchings.

Additionally, we introduce new concepts that model a broad class of fundamental problems and explore fast meta-algorithms for them. These topics include:

• linear programming and the ellipsoid method, and
• matroids, the Greedy algorithm, and matroid intersection.

The goals of this course are to provide a broad overview of fundamental problems in algorithmics and combinatorial optimization. Moreover, participants will develop a strong toolkit for designing and analyzing efficient algorithms, well beyond the standard undergraduate level in algorithm theory.

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

Schwerpunkt: AI, DMI

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: KIKR, DMI.
https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf
Die Veranstaltung findet in Englischer Sprache statt.

Profil: KIKR, DMI.
Schwerpunkt: IMVT-AI, IMVT-DMI, IMVT-VMC
https://lvb.informatik.uni-bremen.de/imvt/03-imvt-cgeom.pdf
Die Veranstaltung beginnt am 10.04.2025.
This course looks at a number of algorithms and data structures from computational geometry with a view on their application to computer graphics. You should feel comfortable with geometric-mathematical thinking as well as algorithmic thinking. We will not need, however, complex mathematics. Die Homepage zur Vorlesung befindet sich immer unter http://cgvr.cs.uni-bremen.de -> Teaching

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

Profil: DMI
Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec.pdf

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

3 SWS Seminar
Raum im KKSB Linz 4
Homepage des KKSB und Uni-Lageplan

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

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 08.04. um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

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

Profil: DMI
Schwerpunkt: IMA-DMI, IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Studenten der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

https://lvb.informatik.uni-bremen.de/ims/03-ims-susai.pdf

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf

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.

https://lvb.informatik.uni-bremen.de/igs/03-ibfw-hto.pdf
A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be formulated as discrete linear optimization problems. This course briefly introduces the theory of such problems. We develop a toolkit to model real-world problems as (discrete) linear programs. We also explore several ways to find integer solutions such as cutting planes, branch & bound, and column generation.

Throughout the course, we learn these skills by modeling and solving, for example, scheduling, packing, matching, routing, and network-design problems. We focus on translating practical examples into mixed-integer linear programs. We learn how to use solvers (such as CPLEX, Gurobi, Xpress and free ones) and tailor the solution process to certain properties of the problem.

This course consists of two phases:

• One week Mon-Fri (full day, 9-5) of lectures and practical labs: July 14-18, in MZH.
• A subsequent project period: One problem has to be modeled, implemented, and solved individually or in a group of at most three students. The topic will be provided by the lecturers and will be discussed on the last day of the block course. The project including the implementation has to be presented in the beginning of the winter semester.

There are no prerequisites except some basic programming skills to participate.

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

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

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

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/ims/03-ims-ais.pdf
Profil: KIKR
Die Veranstaltung findet im DFKI statt.

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

https://lvb.informatik.uni-bremen.de/ims/03-ims-vpur.pdf