Lehrveranstaltungen WiSe 2024/2025
Fachbereich 03: Mathematik/Informatik
Artificial Intelligence and Intelligent Systems, M.Sc.
| 03-AI-F-ATE |
AI Algorithms — Theory and Engineering (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 16:00 - 18:00 MZH 1090 Lecture
wöchentlich Do 08:00 - 10:00 MZH 5600 Exercise
Schwerpunkt: IMVT-AI
|
Nico Hochgeschwender
|
| 03-AI-S-CDF |
Cross-Disciplinary Foundations (in englischer Sprache)
Kurs
ECTS: 6
Termine:
wöchentlich Di 08:00 - 12:00 CART Rotunde - 0.67 Kurs
Masterstudierende der Informatik können das Modul Projektmanagement und Wissenschaftskultur auch über diese Lehrveranstaltung abdecken.
|
Dr. Jörn Syrbe
|
| 03-IMAP-AMAI |
Advanced Methods of AI (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 12:00 - 14:00 MZH 6200 Vorlesung
wöchentlich Mi 16:00 - 18:00 MZH 1100 Übung
|
Michael Beetz
Daniel Beßler
|
| 03-IMAP-AML |
Advanced Machine Learning (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 1090 Übung
wöchentlich Mo 16:00 - 18:00 MZH 1100 Übung
wöchentlich Di 08:00 - 10:00 MZH 1470 Übung
wöchentlich Di 14:00 - 16:00 MZH 1090 Übung
wöchentlich Mi 14:00 - 16:00 MZH 1380/1400 Vorlesung
|
Tanja Schultz
Felix Putze
|
| 03-IMAP-IIS |
Integrated Intelligent Systems (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 16:00 Übung Online
|
Michael Beetz
Dr. Jörn Syrbe
|
| 03-IMS-SHAR |
Hot Topics in Sensors and Human Activity Research (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Do 16:00 - 18:00 MZH 4140 Seminar
|
Dr.-Ing. Hui Liu
|
| 03-IMS-SRSE |
Seminar on Topics in Robot Software Engineering (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Di 12:00 - 14:00 MZH 1450 Seminar
Einzeltermine:
Mi 02.04.25 16:30 - 18:00 TAB 2.57, Am Fallturm 1
|
Nico Hochgeschwender
|
| 03-IMVP-HRI |
Human Robot Interaction (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 16:00 DFKI RH1 A1.03 Vorlesung
wöchentlich Mi 14:00 - 16:00 DFKI RH1 A1.03 Übung
|
Frank Kirchner
Dr. Lisa Gutzeit
|
| 03-IMVP-MLAR |
Machine Learning for autonomous Robots (in englischer Sprache)
Machine Learning for autonomous Robots
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 10:00 - 12:00 DFKI RH1 B0.10 Vorlesung
wöchentlich Do 10:00 - 12:00 Übung
|
Frank Kirchner
Melvin Laux
|
Digitale Medien, B.Sc.
3. Studienjahr
Graduiertenseminare
| 03-IGRAD-CoSy |
Graduiertenseminar Cognitive Systems (in englischer Sprache)
Seminar
Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
|
Thomas Dieter Barkowsky
|
Digitale Medien, M.Sc.
1st academic year
Veranstaltungen von MG ( Media Design) und MT ( Media Theory) finden primär in der HfK statt.
Das Seminar Introduction to Digital Media wird von der HfK angeboten.
Courses of MG ( Media Design) and MT ( Media Theory) take place primarily at the HfK.
The seminar Introduction to Digital Media is offered by the HfK.
M-MI (Media Informatics)
| 03-IMAA-MAD |
Mobile App Development (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 14:00 MZH 1380/1400 Vorlesung
Profil: DMI Schwerpunkt: IMA- DMI https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdfDie Veranstaltung richtet sich an Student*innen 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.
|
Prof. Dr. Rainer Malaka
David Ruh
Nicolas Autzen
Marcus-Sebastian Schröder
|
| 03-IMAA-STMW |
Search Technology for Media & Web (in englischer Sprache)
Search Technology for Media + Web
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 08:00 - 10:00 MZH 1470 Vorlesung
wöchentlich Do 08:00 - 10:00 MZH 6200 Übung
|
Prof. Dr. Sebastian Maneth
M. Sc Yvonne Jenniges
|
| 03-IMAP-VRSIM |
Virtual Reality and Physically-Based Simulation (in englischer Sprache)
Virtuelle Realität und physikalisch-basierte Simulation
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 5600 Vorlesung
wöchentlich Do 10:00 - 12:00 MZH 1100 Übung
Profil: KIKR, DMI Schwerpunkt: IMAP-DMI, IMAP-VMC https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdfEnglish or German. Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets. Some of the topics to be covered (tentatively): • Introduction, basic notions of VR, several example applications • VR technologies: displays, tracking, input devices, scene graphs, game engines • The human visual system and Stereo rendering • Techniques for real-time rendering • Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control • Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc. • Particle systems • Spring-mass systems • Haptics and force feedback • Collision detection • Acoustic rendering The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments. You are encouraged to work on assignments in small teams. https://cgvr.cs.uni-bremen.de/teaching/
|
Prof. Dr. Gabriel Zachmann
|
M-MT (Media Theory)
Additional courses can be found at the HfK website (http://www.hfk-bremen.de/t/digitale-medien).
| 09-71-A.1-1 |
Approaches to Digital Media (in englischer Sprache)
Seminar
Termine:
wöchentlich Mo 16:00 - 18:00 GW1 A0160 (2 SWS)
|
Prof. Dr. Peter Gentzel
|
| 09-71-A.1-2 |
Digital Life (in englischer Sprache)
Seminar
Termine:
wöchentlich Di 12:00 - 14:00 GW2 B1216 (2 SWS)
|
Prof. Dr. Christian Katzenbach
|
M-MA-2 (Special Topics in Digital Media)
All M-MI, M-MD, M-MT courses can be taken as M-MA-2
| 03-IMAA-ITMDS |
IT-Management und Data Science (in englischer Sprache)
IT Management and Data Science
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 14:00 - 18:00 MZH 1090
|
Prof. Dr. Andreas Breiter
Hannah-Marie Büttner
|
2nd academic year
M-MA-32 (Master Project)
| 03-DMM-MA-3-DEJA |
Projekt Dejaview (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 14:00 - 16:00 MZH 1380/1400 Projekt-Plenum
Schwerpunkt: DMI, VMC, Ai
|
Prof. Dr. Gabriel Zachmann
Dr. Rene Weller
|
| 03-DMM-MA-3-ROBRO |
Projekt RoboRoomie (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 14:00 - 16:00 MZH 1470 Projekt-Plenum
Schwerpunkt: DMI
|
Prof. Dr. Rainer Malaka
Rachel Ringe
Bastian Dänekas
Carolin Stellmacher
Nadine Wagener
|
| 03-IMPJ-SMARTB |
Projekt SmartBremen (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 12:00 - 14:00 IW3 0390 Projekt-Plenum
Schwerpunkt: DMI
|
Prof. Dr. Dr. Björn Niehaves
Dr. Gerhard Klassen
Dulce Maria Villegas Nunez
Robin Fritzsche
|
| 03-IMPJ-WELFC |
Projekt WelfareComp (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Di 16:00 - 18:00 MZH 1470 Projekt-Plenum
wöchentlich Fr 08:00 - 10:00 MZH 5600 Projekt-Plenum
|
Prof. Dr. Andreas Breiter
Paola Lopez
|
Graduate Seminars
| 03-IGRAD-CoSy |
Graduiertenseminar Cognitive Systems (in englischer Sprache)
Seminar
Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
|
Thomas Dieter Barkowsky
|
M-MA-2 Special Topics of Digital Media (alt: M-105)
| 09-71-A.1-1 |
Approaches to Digital Media (in englischer Sprache)
Seminar
Termine:
wöchentlich Mo 16:00 - 18:00 GW1 A0160 (2 SWS)
|
Prof. Dr. Peter Gentzel
|
| 09-71-A.1-2 |
Digital Life (in englischer Sprache)
Seminar
Termine:
wöchentlich Di 12:00 - 14:00 GW2 B1216 (2 SWS)
|
Prof. Dr. Christian Katzenbach
|
Industrial Mathematics & Data Analysis, M.Sc.
Foundations (33 CP)
Module: Mathematical Methods for Data Analysis and Image Processing (9 CP)
Compulsory module in which you must attend the following lecture(s):
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
Module: Modeling Project (15 CP)
Compulsory module in which you must attend the following lecture this semester:
| 03-M-MP-2 |
Modeling Project (Part 2) (in englischer Sprache)
Seminar
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
|
Module: Numerical Methods for Partial Differential Equations (9 CP)
Compulsory module in which you must attend the following lecture:
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
Area of Focus: Data Analysis (45 CP)
Area of Focus (27 CP)
The modules Special Topics Data Analysis A and Special Topics Data Analysis B (9 CP each) are mandatory. In addition, EITHER the module Special Topics Data Analysis C OR the module Advanced Communications Data Analysis (9 CP each) must be studied.
Module: Advanced Communications Data Analysis (2 x 4,5 CP = 9 CP)
Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Modules: Special Topics Data Analysis (A, B, and C with 9 CP each)
Compulsory modules in which you must attend one lecture each. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
Extension (18 CP)
Module: Advanced Communications Industrial Mathematics (2 x 4,5 CP = 9 CP)
Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Special Topics Industrial Mathematics A (9 CP)
Compulsory module in which you must attend one lecture. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Area of Focus: Industrial Mathematics (45 CP)
Area of Focus (27 CP)
The modules Special Topics Industrial Mathematics A and Special Topics Industrial Mathematics B (9 CP each) are mandatory. In addition, EITHER the module Special Topics Industrial Mathematics C OR the module Advanced Communications Industrial Mathematics (9 CP each) must be studied.
Module: Advanced Communications Industrial Mathematics (2 x 4,5 CP = 9 CP)
Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Modules: Special Topics Industrial Mathematics (A, B, and C with 9 CP each)
Compulsory modules in which you must attend one lecture each. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Extension (18 CP)
Module: Advanced Communications Data Analysis (2 x 4,5 CP = 9 CP)
Compulsory module in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Special Topics Data Analysis A (9 CP)
Compulsory module in which you must attend one lecture. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
Industriemathematik, B.Sc.
Bachelor 4. Semester und höher
Modul: Fortgeschrittene Themen Industriemathematik (9 CP)
Pflichtmodul, welches im 5. Semester belegt werden sollte. Dazu muss EINE der zugehörigen Veranstaltungen belegt werden, wobei dieses Semester aus folgenden Veranstaltungen gewählt werden kann:
| 03-M-FTH-9 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Mi 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Fr 12:00 - 14:00 Externer Ort: MZH 7200 Übung
Die Veranstaltung finden zusammen statt mit 03-M-SP-26 !
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-FTH-10 |
Basics of mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Do 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Fr 08:00 - 10:00 MZH 1100 Übung
Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt
|
Prof. Dr. Werner Brannath
|
General Studies - Fachergänzende Studien
Fachergänzendes Studienangebot aus der Mathematik bzw. Industriemathematik.
| 03-M-GS-14 |
Starting Data Science in R (in englischer Sprache)
a course on R programming and data science methods with practicals and projects
Praktikum
ECTS: 3
Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) Lecture plus Exercise
The course provides an introductory level of programming skills in R.
Students are welcome to present own ideas, data and projects. I expect a project report or a method talk with demo on own data. Practicals in "R" will work also on synthetic data to illustrate methods features, limitations and differences.
|
Prof. Dr. Stephan Frickenhaus
|
Informatik, B.Sc./M.Sc.
Bachelor Informatik
Bachelor 1. Semester (Vollfach)
Grundlagen Angewandte Informatik
| 03-IBGA-FI-RDL |
Robot Design Lab (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 10:00 - 12:00 DFKI RH1 B0.10 Q & A
wöchentlich Do 10:00 - 12:00 DFKI RH1 B0.10 Übung
wöchentlich Do 14:00 - 16:00 DFKI RH1 B0.10 Übung
|
Frank Kirchner
M. Sc. Mihaela Popescu
M. Sc Jonas Haack
|
Wahlbereich Bachelor-Aufbau (IBA) / Bachelor-Basis (BB)
IBAP / BB-7: Praktische und Technische Informatik
BPO 2020. mindestens ein Lehrangebot aus dieser Kategorie wählen. Auch nutzbar für IBA und IBVP (und Freie Wahl).
BPO 2010: Für ,,Bachelor - PrakTechInfWahl`` zwei Lehrangebote aus dieser Kategorie wählen: Keine Ausnahmeanträge.
IBAP-Lehrangebote auch für \'Informatik-Wahl\' (und Freie Wahl) nutzbar.
| 03-IBAP-RA |
Rechnerarchitektur und Eingebettete Systeme (in englischer Sprache)
Computer Architecture and Embedded Systems
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 1380/1400 Vorlesung
wöchentlich Di 08:00 - 10:00 MZH 1380/1400 Übung
|
Prof. Dr. Rolf Drechsler
Dr. Muhammad Hassan
|
Wahlbereich Bachelor-Vertiefung (IBV) / Bachelor-Ergänzung (BE)
BPO 2010: weitere Lehrangebote für BE unter IBFW
IBV-Lehrangebote regulär für \'Informatik-Wahl\' (und Freie Wahl) nutzbar.
| 03-M-GS-7 |
Introduction to R (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Fr 12:00 - 15:00 LINZ4 40010 Seminar
3 SWS Seminar Die Veranstaltung kann nach BPO'10 als BE-6 angerechnet werden und nach BPO'20 nur in Freie Wahl Homepage des KKSB und Uni-Lageplan
|
Prof. Dr. Werner Brannath
|
Master Informatik
Pflicht Master
Masterstudierende der Informatik können das Modul \'Projektmanagement und Wissenschaftskultur\' auch über diese Lehrveranstaltung abdecken.
| 03-AI-S-CDF |
Cross-Disciplinary Foundations (in englischer Sprache)
Kurs
ECTS: 6
Termine:
wöchentlich Di 08:00 - 12:00 CART Rotunde - 0.67 Kurs
Masterstudierende der Informatik können das Modul Projektmanagement und Wissenschaftskultur auch über diese Lehrveranstaltung abdecken.
|
Dr. Jörn Syrbe
|
Wahlbereich Master-Aufbau (IMA) / Master-Basis (MB)
Nach der Prüfunsordnung von 2020 heißt dieser Bereich Master-Aufbau (IMA), nach der Prüfungsordnung von 2012 Master-Basis (MB).
IMAT / MB-6 - Theoretische Informatik und Mathematik
Nach MPO 2020 und MPO 2012 mindestens ein Lehrangebot aus dieser Kategorie wählen.
Nach MPO 2012 auf Antrag auch IMVT-Lehrangebot oder fortgeschrittenes Mathematik-Lehrangebot möglich.
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-IMAT-IRQ |
Introduction to Reversible and Quantum Computing (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5500 MZH 3150 Vorlesung
wöchentlich Di 08:00 - 10:00 MZH 5500 Übung
|
Prof. Dr. Rolf Drechsler
Dr. Kamalika Datta
Dr. Abhoy Kole
|
| 03-IMAT-STMT |
Set Theory and Model Theory (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 3150 Vorlesung
wöchentlich Mi 12:00 - 14:00 MZH 3150 Übung
Profil: SQ Schwerpunkt: IMVT-SQ https://lvb.informatik.uni-bremen.de/imat/03-imat-stmt.pdfSet theory and model theory Intuitively, a set is a collection of all elements that satisfy a certain property. This intuition, however, is false! The following example is known as Russell's Paradox. Consider the set S whose elements are exactly those that are not members of themselves: S = { X : X is not element of X }. Is S an element of S? If S is an element of S, then S is not an element of S. On the other hand, if S is not an element of S, then S belongs to S. In either case we have a contradiction. We must revise our intuitive notion of a set. In the first part of the lecture we develop axiomatic set theory (ZFC) in the framework of first-order logic, which forms the foundation of modern mathematics. We cover the axioms of set theory, ordinal numbers and induction and recursion over well-founded relations, cardinal numbers and the axiom of choice. In the second part of the lecture we turn to classical topics of first-order model theory. Model theory studies classes of mathematical structures, such as groups, fields, or graphs, from the point of view of mathematical logic. Many notions, such as homomorphisms, substructures, or free structures, that are commonly studied in specific fields of mathematics are unified by the general approach of model theory. We study ways to construct models with desired properties from first-order theories and the expressive power of first-order logic.
|
Prof. Dr. Sebastian Siebertz
|
IMAP / MB-7 - Praktische und technische Informatik
Nach MPO 2020 mindestens ein Lehrangebot aus dieser Kategorie wählen. Nach MPO 2012 zwei Lehrangebote aus dieser Kategorie wählen.
| 03-IMAP-AMAI |
Advanced Methods of AI (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 12:00 - 14:00 MZH 6200 Vorlesung
wöchentlich Mi 16:00 - 18:00 MZH 1100 Übung
|
Michael Beetz
Daniel Beßler
|
| 03-IMAP-AML |
Advanced Machine Learning (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 1090 Übung
wöchentlich Mo 16:00 - 18:00 MZH 1100 Übung
wöchentlich Di 08:00 - 10:00 MZH 1470 Übung
wöchentlich Di 14:00 - 16:00 MZH 1090 Übung
wöchentlich Mi 14:00 - 16:00 MZH 1380/1400 Vorlesung
|
Tanja Schultz
Felix Putze
|
| 03-IMAP-IIS |
Integrated Intelligent Systems (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 16:00 Übung Online
|
Michael Beetz
Dr. Jörn Syrbe
|
| 03-IMAP-VRSIM |
Virtual Reality and Physically-Based Simulation (in englischer Sprache)
Virtuelle Realität und physikalisch-basierte Simulation
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 5600 Vorlesung
wöchentlich Do 10:00 - 12:00 MZH 1100 Übung
Profil: KIKR, DMI Schwerpunkt: IMAP-DMI, IMAP-VMC https://lvb.informatik.uni-bremen.de/imap/03-imap-vrsim.pdfEnglish or German. Over the past two decades, VR has established itself as an important tool in several industries, such as manufacturing (e.g., automotive, airspace, ship building), architecture, and pharmaceutical industries. During the past few years, we have been witnessing the second "wave" of VR, this time in the consumer, in particular, in the entertainment markets. Some of the topics to be covered (tentatively): • Introduction, basic notions of VR, several example applications • VR technologies: displays, tracking, input devices, scene graphs, game engines • The human visual system and Stereo rendering • Techniques for real-time rendering • Fundamental immersive interaction techniques: fundamentals and principles, 3D navigation, user models, 3D selection, redirected walking, system control • Complex immersive interaction techniques: world-in-miniature, action-at-a-distance, magic lens, etc. • Particle systems • Spring-mass systems • Haptics and force feedback • Collision detection • Acoustic rendering The assignments will be mostly practical ones, based on the cross-platform game engine Unreal. Participants will start developing with "visual programming", and later use C++ to solve the assignments. You are encouraged to work on assignments in small teams. https://cgvr.cs.uni-bremen.de/teaching/
|
Prof. Dr. Gabriel Zachmann
|
IMAA / MB-8 - Angewandte Informatik
Nach MPO 2012 ein Lehrangebot aus dieser Kategorie wählen; auf Antrag auch IMVA-Lehrangebot möglich.
| 03-IMAA-ITMDS |
IT-Management und Data Science (in englischer Sprache)
IT Management and Data Science
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 14:00 - 18:00 MZH 1090
|
Prof. Dr. Andreas Breiter
Hannah-Marie Büttner
|
| 03-IMAA-MAD |
Mobile App Development (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 14:00 MZH 1380/1400 Vorlesung
Profil: DMI Schwerpunkt: IMA- DMI https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdfDie Veranstaltung richtet sich an Student*innen 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.
|
Prof. Dr. Rainer Malaka
David Ruh
Nicolas Autzen
Marcus-Sebastian Schröder
|
| 03-IMAA-STMW |
Search Technology for Media & Web (in englischer Sprache)
Search Technology for Media + Web
Vorlesung
ECTS: 6
Termine:
wöchentlich Mi 08:00 - 10:00 MZH 1470 Vorlesung
wöchentlich Do 08:00 - 10:00 MZH 6200 Übung
|
Prof. Dr. Sebastian Maneth
M. Sc Yvonne Jenniges
|
Wahlbereich Master-Vertiefung (IMV) / Master-Ergänzung (ME)
MPO 2012: weitere ME-Angebote unter Wahlbereich IMS/ME und unter General Studies IMGS
IMVP / ME-7 - Praktische Informatik
| 03-IMVP-DLS |
Digital Logic Synthesis (in englischer Sprache)
Kurs
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 1110 Kurs
wöchentlich Mi 10:00 - 12:00 MZH 1100 Kurs
|
Prof. Dr. Rolf Drechsler
Dr. Chandan Kumar Jha
|
| 03-IMVP-HRI |
Human Robot Interaction (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 16:00 DFKI RH1 A1.03 Vorlesung
wöchentlich Mi 14:00 - 16:00 DFKI RH1 A1.03 Übung
|
Frank Kirchner
Dr. Lisa Gutzeit
|
| 03-IMVP-MLAR |
Machine Learning for autonomous Robots (in englischer Sprache)
Machine Learning for autonomous Robots
Vorlesung
ECTS: 6
Termine:
wöchentlich Di 10:00 - 12:00 DFKI RH1 B0.10 Vorlesung
wöchentlich Do 10:00 - 12:00 Übung
|
Frank Kirchner
Melvin Laux
|
Wahlbereich IMS / ME - Master Seminare
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-IMS-SHAR |
Hot Topics in Sensors and Human Activity Research (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Do 16:00 - 18:00 MZH 4140 Seminar
|
Dr.-Ing. Hui Liu
|
| 03-IMS-SRSE |
Seminar on Topics in Robot Software Engineering (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Di 12:00 - 14:00 MZH 1450 Seminar
Einzeltermine:
Mi 02.04.25 16:30 - 18:00 TAB 2.57, Am Fallturm 1
|
Nico Hochgeschwender
|
Wahlbereich IMPJ - Master-Projekte
| 03-DMM-MA-3-DEJA |
Projekt Dejaview (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 14:00 - 16:00 MZH 1380/1400 Projekt-Plenum
Schwerpunkt: DMI, VMC, Ai
|
Prof. Dr. Gabriel Zachmann
Dr. Rene Weller
|
| 03-DMM-MA-3-ROBRO |
Projekt RoboRoomie (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 14:00 - 16:00 MZH 1470 Projekt-Plenum
Schwerpunkt: DMI
|
Prof. Dr. Rainer Malaka
Rachel Ringe
Bastian Dänekas
Carolin Stellmacher
Nadine Wagener
|
| 03-IMPJ-IACMR |
Projekt Increasing Autonomy Capacilities of Marine Robots (in englischer Sprache)
Projektplenum
ECTS: 12
Termine:
wöchentlich Fr 14:00 - 16:00 Externer Ort: DFKI Projekt-Plenum
Schwerpunkt: AI
Das Plenum am Freitag findet im DFKI statt.
|
Frank Kirchner
Dr. Bilal Wehbe
|
| 03-IMPJ-SMARTB |
Projekt SmartBremen (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Fr 12:00 - 14:00 IW3 0390 Projekt-Plenum
Schwerpunkt: DMI
|
Prof. Dr. Dr. Björn Niehaves
Dr. Gerhard Klassen
Dulce Maria Villegas Nunez
Robin Fritzsche
|
| 03-IMPJ-SUT |
Projekt SUTURO (in englischer Sprache)
(WiSe 24/25 bis SoSe 2025)
Projektplenum
ECTS: 12
Termine:
wöchentlich Fr 12:00 - 14:00 MZH 1110 Projekt-Plenum
Schwerpunkt: AI
|
Michael Beetz
|
| 03-IMPJ-WELFC |
Projekt WelfareComp (in englischer Sprache)
(WiSe 24/25)
Projektplenum
ECTS: 30
Termine:
wöchentlich Di 16:00 - 18:00 MZH 1470 Projekt-Plenum
wöchentlich Fr 08:00 - 10:00 MZH 5600 Projekt-Plenum
|
Prof. Dr. Andreas Breiter
Paola Lopez
|
Graduiertenseminare
| 03-IGRAD-CoSy |
Graduiertenseminar Cognitive Systems (in englischer Sprache)
Seminar
Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
|
Thomas Dieter Barkowsky
|
Sonstige Veranstaltungen ohne Kreditpunkte
| 03-ISONST-EJC |
EDM Journal Club (in englischer Sprache)
Seminar
Einzeltermine:
Mo 10.02.20 14:00 - 16:00 MZH 5300
Veranstaltung für Doktoranten, jeden 1. Montag im Monat von 14-16h in Raum 5300.
|
Robert Porzel
Sebastian Höffner
Dr. Nina Wenig
Prof. Dr. Rainer Malaka
|
Mathematics, M.Sc.
Area of Specialization: Algebra
Modules: Specialization (A, B, and C with 9 CP each)
The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
Modules: Diversification (A, B, and C with 9 CP each)
The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-37 |
Spectral Geometry of Hyperbolic Surfaces (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5410 Lecture
wöchentlich Mo 16:00 - 18:00 MZH 5410 Exercise
wöchentlich Di 12:00 - 14:00 MZH 5410 Lecture
Einzeltermine:
Mo 03.02.25 16:00 - 18:00 MZH 5410
|
Claudio Meneses-Torres
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Module: Advanced Communications A (2 x 4,5 CP = 9 CP)
Compulsory module in the area of specialization in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-M-AC-30 |
Geometry of Polynomials (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Einzeltermine:
Mi 22.01.25 10:00 - 12:00 MZH 5410
The study of the geometry of univariate polynomials has roots in the pioneering work of Gauss in the early 19th century and has been advanced by numerous distinguished mathematicians over the years. Despite its long-standing history, this field continues to present intriguing challenges and notable conjectures.
|
Eugenia Saorin Gomez
|
Module: Advanced Communications B (2 x 4,5 CP = 9 CP)
Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-22 |
Advanced Communication Analysis (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5600 Seminar
Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-27 |
Exponential Families (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5500 Seminar
This is a seminar in the specialization area "Stochastics / Statistics". The seminar deals with (univariate and multivariate) exponential families, which arguably constitute the most important classes of statistical models.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Reading Course A (9 CP)
Compulsory module in the area of specialization and with the following course:
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
Module: Reading Course B (9 CP)
Compulsory module either in the area of specialization or area of diversification and with the following courses:
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Area of Specialization: Analysis
Modules: Specialization (A, B, and C with 9 CP each)
The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-M-SP-37 |
Spectral Geometry of Hyperbolic Surfaces (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5410 Lecture
wöchentlich Mo 16:00 - 18:00 MZH 5410 Exercise
wöchentlich Di 12:00 - 14:00 MZH 5410 Lecture
Einzeltermine:
Mo 03.02.25 16:00 - 18:00 MZH 5410
|
Claudio Meneses-Torres
|
Modules: Diversification (A, B, and C with 9 CP each)
The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Module: Advanced Communications A (2 x 4,5 CP = 9 CP)
Compulsory module in the area of specialization and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-22 |
Advanced Communication Analysis (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5600 Seminar
Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
Module: Advanced Communications B (2 x 4,5 CP = 9 CP)
Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-27 |
Exponential Families (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5500 Seminar
This is a seminar in the specialization area "Stochastics / Statistics". The seminar deals with (univariate and multivariate) exponential families, which arguably constitute the most important classes of statistical models.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-30 |
Geometry of Polynomials (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Einzeltermine:
Mi 22.01.25 10:00 - 12:00 MZH 5410
The study of the geometry of univariate polynomials has roots in the pioneering work of Gauss in the early 19th century and has been advanced by numerous distinguished mathematicians over the years. Despite its long-standing history, this field continues to present intriguing challenges and notable conjectures.
|
Eugenia Saorin Gomez
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Reading Course A (9 CP)
Compulsory module in the area of specialization and with the following course:
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
Module: Reading Course B (9 CP)
Compulsory module either in the area of specialization or area of diversification and with the following courses:
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Area of Specialization: Numerical Analysis
Modules: Specialization (A, B, and C with 9 CP each)
The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Modules: Diversification (A, B, and C with 9 CP each)
The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-37 |
Spectral Geometry of Hyperbolic Surfaces (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5410 Lecture
wöchentlich Mo 16:00 - 18:00 MZH 5410 Exercise
wöchentlich Di 12:00 - 14:00 MZH 5410 Lecture
Einzeltermine:
Mo 03.02.25 16:00 - 18:00 MZH 5410
|
Claudio Meneses-Torres
|
Module: Advanced Communications A (2 x 4,5 CP = 9 CP)
Compulsory module in the area of specialization and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Advanced Communications B (2 x 4,5 CP = 9 CP)
Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-22 |
Advanced Communication Analysis (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5600 Seminar
Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-27 |
Exponential Families (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5500 Seminar
This is a seminar in the specialization area "Stochastics / Statistics". The seminar deals with (univariate and multivariate) exponential families, which arguably constitute the most important classes of statistical models.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-AC-30 |
Geometry of Polynomials (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Einzeltermine:
Mi 22.01.25 10:00 - 12:00 MZH 5410
The study of the geometry of univariate polynomials has roots in the pioneering work of Gauss in the early 19th century and has been advanced by numerous distinguished mathematicians over the years. Despite its long-standing history, this field continues to present intriguing challenges and notable conjectures.
|
Eugenia Saorin Gomez
|
Module: Reading Course B (9 CP)
Compulsory module either in the area of specialization or area of diversification and with the following courses:
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Area of Specialization: Statistics/Stochastics
Modules: Specialization (A, B, and C with 9 CP each)
The modules Specialization A and Specialization B are compulsory modules (2 x 9 CP = 18 CP). The module Specialization C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
Modules: Diversification (A, B, and C with 9 CP each)
The modules Diversification A and Diversification B are compulsory modules (2 x 9 CP = 18 CP). The module Diversification C (9 CP) is a compulsory elective module. This semester you can choose from the following lectures:
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-37 |
Spectral Geometry of Hyperbolic Surfaces (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5410 Lecture
wöchentlich Mo 16:00 - 18:00 MZH 5410 Exercise
wöchentlich Di 12:00 - 14:00 MZH 5410 Lecture
Einzeltermine:
Mo 03.02.25 16:00 - 18:00 MZH 5410
|
Claudio Meneses-Torres
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Module: Advanced Communications A (2 x 4,5 CP = 9 CP)
Compulsory module in the area of specialization and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-27 |
Exponential Families (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5500 Seminar
This is a seminar in the specialization area "Stochastics / Statistics". The seminar deals with (univariate and multivariate) exponential families, which arguably constitute the most important classes of statistical models.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
Module: Advanced Communications B (2 x 4,5 CP = 9 CP)
Compulsory module in the area of diversification and in which you must attend a total of two seminars with 4,5 CP each. This semester you can choose from the following seminars:
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-22 |
Advanced Communication Analysis (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5600 Seminar
Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-30 |
Geometry of Polynomials (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Einzeltermine:
Mi 22.01.25 10:00 - 12:00 MZH 5410
The study of the geometry of univariate polynomials has roots in the pioneering work of Gauss in the early 19th century and has been advanced by numerous distinguished mathematicians over the years. Despite its long-standing history, this field continues to present intriguing challenges and notable conjectures.
|
Eugenia Saorin Gomez
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Module: Reading Course A (9 CP)
Compulsory module in the area of specialization and with the following course:
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Module: Reading Course B (9 CP)
Compulsory module either in the area of specialization or area of diversification and with the following courses:
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Mathematik, B.Sc.
Bachelor 4. Semester und höher
Module: Fortgeschrittene Themen (A, B und C mit je 9 CP)
Pflichtmodule, welche im 4. und 5. Semester belegt werden sollten. Für jedes der DREI Module (Fortgeschrittene Themen A / B / C) muss jeweils EINE der zugehörigen Veranstaltungen belegt werden, wobei dieses Semester aus folgenden Veranstaltungen gewählt werden kann:
| 03-M-FTH-9 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Mi 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Fr 12:00 - 14:00 Externer Ort: MZH 7200 Übung
Die Veranstaltung finden zusammen statt mit 03-M-SP-26 !
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-FTH-10 |
Basics of mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Do 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Fr 08:00 - 10:00 MZH 1100 Übung
Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt
|
Prof. Dr. Werner Brannath
|
General Studies - Fachergänzende Studien
Fachergänzendes Studienangebot aus der Mathematik bzw. Industriemathematik
| 03-M-GS-14 |
Starting Data Science in R (in englischer Sprache)
a course on R programming and data science methods with practicals and projects
Praktikum
ECTS: 3
Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) Lecture plus Exercise
The course provides an introductory level of programming skills in R.
Students are welcome to present own ideas, data and projects. I expect a project report or a method talk with demo on own data. Practicals in "R" will work also on synthetic data to illustrate methods features, limitations and differences.
|
Prof. Dr. Stephan Frickenhaus
|
Mathematik, B.Sc./M.Sc. (Studienbeginn vor 2022)
Bachelor: Wahlpflichtveranstaltungen
Wahlpflichtveranstaltungen für den Studiengang Mathematik B.Sc.
| 03-M-FTH-9 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Mi 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Fr 12:00 - 14:00 Externer Ort: MZH 7200 Übung
Die Veranstaltung finden zusammen statt mit 03-M-SP-26 !
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-FTH-10 |
Basics of mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Do 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Fr 08:00 - 10:00 MZH 1100 Übung
Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt
|
Prof. Dr. Werner Brannath
|
Master: Wahlpflichtveranstaltungen
Vertiefungsrichtung Algebra
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
Vertiefungsrichtung Analysis
| 03-M-SP-37 |
Spectral Geometry of Hyperbolic Surfaces (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 5410 Lecture
wöchentlich Mo 16:00 - 18:00 MZH 5410 Exercise
wöchentlich Di 12:00 - 14:00 MZH 5410 Lecture
Einzeltermine:
Mo 03.02.25 16:00 - 18:00 MZH 5410
|
Claudio Meneses-Torres
|
Vertiefungsrichtung Numerik
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Vertiefungsrichtung Stochastik & Statistik
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
Master: Seminare
Vertiefungsrichtung Algebra
| 03-M-AC-30 |
Geometry of Polynomials (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Einzeltermine:
Mi 22.01.25 10:00 - 12:00 MZH 5410
The study of the geometry of univariate polynomials has roots in the pioneering work of Gauss in the early 19th century and has been advanced by numerous distinguished mathematicians over the years. Despite its long-standing history, this field continues to present intriguing challenges and notable conjectures.
|
Eugenia Saorin Gomez
|
Vertiefungsrichtung Analysis
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-22 |
Advanced Communication Analysis (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5600 Seminar
Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
Vertiefungsrichtung Numerik
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
Vertiefungsrichtung Stochastik & Statistik
| 03-M-AC-26 |
Analysis/Stochastics/Statistics (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Mi 12:00 - 14:00 MZH 4140 Seminar
|
Prof. Dr. Marc Keßeböhmer
|
| 03-M-AC-27 |
Exponential Families (in englischer Sprache)
Seminar
ECTS: 4,5/6
Termine:
wöchentlich Di 14:00 - 16:00 MZH 5500 Seminar
This is a seminar in the specialization area "Stochastics / Statistics". The seminar deals with (univariate and multivariate) exponential families, which arguably constitute the most important classes of statistical models.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
Master: Reading Courses
| 03-M-RC-ALG |
Reading Course Algebra (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-RC-ANA |
Reading Course Analysis (in englischer Sprache)
Seminar
ECTS: 9
In the Reading Course Analysis advanced topics in the area of analysis are discussed. The precise topic for the Winter Semester 2024/25 will be decided upon with the participants.
|
Prof. Dr. Anke Dorothea Pohl
|
| 03-M-RC-STS |
Reading Course Statistics/Stochastics (in englischer Sprache)
Seminar
ECTS: 9
|
Prof. Dr. Werner Brannath
Prof. Dr. Thorsten-Ingo Dickhaus
|
Oberseminare
|
Oberseminar Mathematical Parameter Identification (RTG-Seminar) (in englischer Sprache)
Research Seminar - Mathematical Parameter Identification (RTG)
Seminar
Termine:
zweiwöchentlich (Startwoche: 1) Mi 12:00 - 14:00 MZH 2490 (Seminarraum) Seminar
|
Dr. rer. nat. Pascal Fernsel
|
General Studies
| 03-M-GS-5 |
Statistical Consulting (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Mo 10:00 - 12:00 Seminar
|
Dr. Martin Scharpenberg
|
| 03-M-GS-7 |
Introduction to R (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Fr 12:00 - 15:00 LINZ4 40010 Seminar
3 SWS Seminar Die Veranstaltung kann nach BPO'10 als BE-6 angerechnet werden und nach BPO'20 nur in Freie Wahl Homepage des KKSB und Uni-Lageplan
|
Prof. Dr. Werner Brannath
|
| 03-M-GS-14 |
Starting Data Science in R (in englischer Sprache)
a course on R programming and data science methods with practicals and projects
Praktikum
ECTS: 3
Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) Lecture plus Exercise
The course provides an introductory level of programming skills in R.
Students are welcome to present own ideas, data and projects. I expect a project report or a method talk with demo on own data. Practicals in "R" will work also on synthetic data to illustrate methods features, limitations and differences.
|
Prof. Dr. Stephan Frickenhaus
|
Medical Biometry / Biostatistics, M.Sc.
Modulbereich Biometrie
| 03-BioStat-A-1-1 |
Biometrical Methods (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 14:00 - 15:00 LINZ4 40010 Exercise
wöchentlich Mo 15:00 - 16:00 LINZ4 40010 Exercise
wöchentlich Di 09:00 - 10:00 BIPS 1550 Lecture
wöchentlich Do 14:00 - 16:00 BIPS 1550 Lecture
Einzeltermine:
Fr 17.01.25 10:00 - 12:00 BIPS 1550
Mo 03.03.25 14:00 - 16:00 BIPS 2690
Only the lecture on Thursday 07th November 2024 will take place in Linz 4. All other lectures will take place in BIPS 1550.
|
Prof. Dr. Marvin Nils Ole Wright
|
| 03-BioStat-A-2-1 |
Statistical Modelling I (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 12:00 - 14:00 Externer Ort: BIPS 1550 Exercise
wöchentlich Di 14:00 - 16:00 BIPS 1550 Lecture
wöchentlich Mi 10:00 - 12:00 Lecture
Einzeltermine:
Mo 21.10.24 13:00 - 14:00
Di 22.10.24 14:00 - 15:00
Mi 23.10.24 11:00 - 12:00
Mo 28.10.24 13:00 - 14:00
Mi 30.10.24 11:00 - 12:00
Mo 04.11.24 13:00 - 14:00 BIPS 1.550
Di 05.11.24 14:00 - 16:00 BIPS 2.690 (second floor)
Mo 11.11.24 13:00 - 14:00
Mo 18.11.24 13:00 - 14:00
Di 03.12.24 14:00 - 16:00
Mo 09.12.24 13:00 - 14:00
Mo 16.12.24 13:00 - 14:00
Mi 18.12.24 11:00 - 12:00
Mo 06.01.25 13:00 - 14:00
Mi 08.01.25 10:00 - 12:00
Mo 13.01.25 13:00 - 14:00
Mi 15.01.25 11:00 - 12:00
Mo 20.01.25 13:00 - 14:00
Mi 22.01.25 10:00 - 12:00
Mo 27.01.25 13:00 - 14:00
|
Prof. Dr. Iris Pigeot-Kübler
|
| 03-BioStat-A-3-1 |
Data Management (in englischer Sprache)
Praktikum
ECTS: 4
Termine:
wöchentlich Do 08:00 - 10:00 MZH 5500 Praktikum
wöchentlich Do 10:00 - 12:00 MZH 5500 Praktikum
|
Dr. Martin Scharpenberg
|
Modulbereich Anwendungsfelder und biomedizinische Grundlagen
| 03-BioStat-B-1-1 |
Clinical Trials I (in englischer Sprache)
Vorlesung
ECTS: 4
Termine:
wöchentlich Di 10:00 - 12:00 LINZ4 40010 Lecture
wöchentlich Di 13:00 - 14:00 LINZ4 40010 Exercise
wöchentlich Mi 13:00 - 14:00 LINZ4 40010 Exercise
Einzeltermine:
Mi 29.01.25 08:00 - 10:00 SFG 1010
|
Max Westphal
|
| 03-BioStat-B-1-3 |
Ethical Aspects, Laws and Guidelines (in englischer Sprache)
Vorlesung
ECTS: 3
Termine:
wöchentlich Mo 16:00 - 18:00 LINZ4 40010 Lecture
Einzeltermine:
Fr 07.02.25 10:00 - 12:00 MZH 1470
|
Dr. Martin Scharpenberg
|
| 03-BioStat-B-2-1 |
Medical Basics - Common Diseases and Molecular Medicine (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Fr 09:00 - 12:00 LINZ4 40010 Lecture
Einzeltermine:
Do 16.01.25 13:00 - 16:00 LINZ4 40010
Mo 27.01.25 09:00 - 12:00 LINZ4 40010
Fr 14.02.25 10:00 - 12:00 MZH 1090
Die Vorlesung findet im LINZ4 4010 statt. Lecturer (DozentInnen): MedizinerInnen des Kooperationszentrums Medizin der Universität Bremen (KOM) Homepage
|
Nikolaos Papathanasiou
Dr. Martin Scharpenberg
Bernd Mühlbauer
|
Wahlbereich
| 03-BioStat-E-1 |
Mathematical Basics in Biostatistics (in englischer Sprache)
Vorlesung
ECTS: 3
Termine:
wöchentlich Mi 14:00 - 16:00 LINZ4 40010 Lecture
wöchentlich Mi 16:00 - 17:00 LINZ4 40010 Exercise
|
Prof. Dr. Werner Brannath
|
Sonstige Veranstaltungen
| 03-BioStat-VORKURS |
Schulung Statistiksoftware SAS (in englischer Sprache)
Blockveranstaltung
Einzeltermine:
Mo 07.10.24 - Do 10.10.24 (Mo, Di, Mi, Do) 09:00 - 16:00 COG0320
|
Nils Fabian Gesing
Alina Ludewig
|
| 03-M-GS-5 |
Statistical Consulting (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Mo 10:00 - 12:00 Seminar
|
Dr. Martin Scharpenberg
|
| 03-M-GS-7 |
Introduction to R (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Fr 12:00 - 15:00 LINZ4 40010 Seminar
3 SWS Seminar Die Veranstaltung kann nach BPO'10 als BE-6 angerechnet werden und nach BPO'20 nur in Freie Wahl Homepage des KKSB und Uni-Lageplan
|
Prof. Dr. Werner Brannath
|
Technomathematik, B.Sc./M.Sc. (Studienbeginn vor 2022)
Bachelor: Wahlpflichtveranstaltungen
| 03-M-FTH-9 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Mi 08:00 - 10:00 Externer Ort: MZH 7200 Vorlesung
wöchentlich Fr 12:00 - 14:00 Externer Ort: MZH 7200 Übung
Die Veranstaltung finden zusammen statt mit 03-M-SP-26 !
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-FTH-10 |
Basics of mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Do 08:00 - 10:00 LINZ4 40010 Lecture
wöchentlich Fr 08:00 - 10:00 MZH 1100 Übung
Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt
|
Prof. Dr. Werner Brannath
|
Master: Pflichtveranstaltungen
| 03-M-NPDE-1 |
Numerical Methods for Partial Differential Equations (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 Companion Course (MZH 2490)
wöchentlich Di 10:00 - 12:00 MZH 2340 Lecture
wöchentlich Di 12:00 - 14:00 MZH 2340 Exercise
wöchentlich Do 12:00 - 14:00 MZH 2340 Lecture
The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. The connection of theory, numerical analysis and implementation is particularly important. The numerical algorithms are to be implemented in programming tasks under guidance.
|
Alfred Schmidt
|
Master: Wahlpflichtveranstaltungen
| 03-IMAT-AU |
Algorithms and Uncertainty (in englischer Sprache)
Kurs
ECTS: 6 (9)
Termine:
wöchentlich Di 12:00 - 14:00 MZH 5600 Kurs
wöchentlich Do 08:00 - 10:00 MZH 1470 Kurs
Profil: SQ Schwerpunkt: IMA-SQ, IMA-AI https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdfA key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data. In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms. Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions. Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.
|
Prof. Dr. Nicole Megow
|
| 03-M-MDAIP-1 |
Mathematical Methods for Data Analysis and Image Processing (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 12:00 - 14:00 MZH 1470 Lecture
wöchentlich Do 10:00 - 12:00 MZH 1470 Lecture
wöchentlich Do 14:00 - 16:00 MZH 6200 Exercise
|
Dirk Lorenz
|
| 03-M-SP-2 |
Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Do 08:00 - 10:00 Externer Ort: LINZ4 4010 Lecture
wöchentlich Fr 08:00 - 10:00 Externer Ort: MZH 1100 Exercise
Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.
|
Prof. Dr. Werner Brannath
|
| 03-M-SP-26 |
Algebraic Topology (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 7200 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 7200 Exercise
Findet zusammen mit der LV 03-M-FTH-9 statt.
|
Prof. Dr. Dmitry Feichtner-Kozlov
|
| 03-M-SP-28 |
Mathematical Concepts of Risk Management (Statistics III) (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Exercise
wöchentlich Di 10:00 - 12:00 MZH 7200 Lecture
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture
The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.
|
Prof. Dr. Thorsten-Ingo Dickhaus
|
| 03-M-SP-38 |
Finite Elements - Selected Chapters (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Di 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 08:00 - 10:00 MZH 2340 Lecture
wöchentlich Mi 10:00 - 12:00 MZH 2340 Exercise
The finite element method is used for the discretisation of partial differential equations in many different applications. In this course we will deepen existing knowledge in finite element methods with respect to different applications and learn new techniques to increase their computational speed.
|
Prof. Dr. Andreas Rademacher
|
| 03-M-SP-39 |
Advanced Topics in Image Processing - The Beauty of Variational Calculus (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 4140 Lecture
wöchentlich Fr 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Fr 14:00 - 16:00 MZH 2340 Exercise
This course focusses on two advanced tools in modern mathematical image processing, namely the direct method of variational calculus and the iterated soft thresholding algorithm.
|
Peter Maaß
Dr. Matthias Beckmann
Dr. rer. nat. Pascal Fernsel
|
| 03-M-SP-40 |
Convex Analysis and Optimization (in englischer Sprache)
Vorlesung
ECTS: 9
Termine:
wöchentlich Mo 14:00 - 16:00 MZH 2340 Lecture
wöchentlich Mi 12:00 - 14:00 MZH 2340 Lecture
wöchentlich Mi 14:00 - 16:00 MZH 2340 Exercise
|
Dirk Lorenz
|
| 04-M30-CP-SFT-3 |
Trajectory Optimization (in englischer Sprache)
Vorlesung
ECTS: 4,5
Termine:
wöchentlich Mo 14:00 - 18:00 FZB 0240
Einzeltermine:
Mi 05.03.25 10:00 - 13:00 SFG 0140
|
Prof. Dr. Christof Büskens
Matthias Knauer
|
Master: Seminare
| 03-IMS-RTAT |
Recent Trends in Algorithm Theory (in englischer Sprache)
Blockveranstaltung
ECTS: 3 (4,5 / 6)
Frequency: The seminar is planned as a block seminar, meaning that all talks will be at up to three days, most likely in the middle of December. We will discuss this in the first meeting. The first (organizational) meeting is planned as follows: Wednesday, October 16, at 14:00 pm in the room MZH 3150.
Learning Outcome:
Students learn how recent advances in algorithm theory can be used to improve state-of-the-art algorithms to obtain faster, better or new types of algorithms. They learn about relevant problems that are important and used in many applications. The main goals are to understand, design, and analyze algorithms for solving such problems.
Furthermore, the students will learn how to read and thoroughly understand original research papers. They learn how to prepare slides for these papers and give an oral presentation to other students who have no prior knowledge about the paper.
Contents: This seminar focuses on recent advances in algorithm theory. Most topics considered include important problems on graphs, typically related to optimization problems. These advances include faster running times of the algorithms, solutions with improved performance guarantees or new concepts in algorithm design, such as algorithms with machine-learned predictions.
|
Dr. Felix Christian Hommelsheim
|
| 03-M-AC-5 |
Mathematical Methods in Machine Learning (in englischer Sprache)
Seminar
ECTS: 4,5/ 6
Termine:
wöchentlich Fr 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
Dr. Matthias Beckmann
|
| 03-M-AC-28 |
Advanced Numerical Methods for Partial Differential Equations (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
This is a seminar with subjects from numerical methods for PDEs, expecially finite element methods, with applications to real world problems.
|
Alfred Schmidt
|
| 03-M-AC-31 |
Introduction to Robust Control (in englischer Sprache)
Seminar
ECTS: 4,5 / 6
Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1450 Seminar
Einzeltermine:
Fr 31.01.25 14:00 - 17:00 MZH 5600
Mo 03.02.25 10:00 - 14:00 MZH 5600
|
Dr. Chathura Wanigasekara
|
| 03-M-MP-2 |
Modeling Project (Part 2) (in englischer Sprache)
Seminar
ECTS: 9
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2340 Seminar
|
Peter Maaß
|
Oberseminare
|
Oberseminar Mathematical Parameter Identification (RTG-Seminar) (in englischer Sprache)
Research Seminar - Mathematical Parameter Identification (RTG)
Seminar
Termine:
zweiwöchentlich (Startwoche: 1) Mi 12:00 - 14:00 MZH 2490 (Seminarraum) Seminar
|
Dr. rer. nat. Pascal Fernsel
|
General Studies
| 03-M-GS-5 |
Statistical Consulting (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Mo 10:00 - 12:00 Seminar
|
Dr. Martin Scharpenberg
|
| 03-M-GS-7 |
Introduction to R (in englischer Sprache)
Seminar
ECTS: 3
Termine:
wöchentlich Fr 12:00 - 15:00 LINZ4 40010 Seminar
3 SWS Seminar Die Veranstaltung kann nach BPO'10 als BE-6 angerechnet werden und nach BPO'20 nur in Freie Wahl Homepage des KKSB und Uni-Lageplan
|
Prof. Dr. Werner Brannath
|
Systems Engineering, B.Sc. / M.Sc.
| 03-IMAP-AML |
Advanced Machine Learning (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 MZH 1090 Übung
wöchentlich Mo 16:00 - 18:00 MZH 1100 Übung
wöchentlich Di 08:00 - 10:00 MZH 1470 Übung
wöchentlich Di 14:00 - 16:00 MZH 1090 Übung
wöchentlich Mi 14:00 - 16:00 MZH 1380/1400 Vorlesung
|
Tanja Schultz
Felix Putze
|
Wirtschaftsinformatik, B.Sc.
2./3. Studienjahr
Wahlmodule
Schwerpunkt "Computational Finance"
WI-CF-WP
Auflistung der WInf-Schwerpunkt-Wahlmodule siehe unter WInf-Wahlmodule
| 07-BA37-231-04 |
Behavioral Finance (in englischer Sprache)
Seminar
ECTS: 6
Termine:
wöchentlich Do 10:00 - 12:00 WiWi1 A1100
Einzeltermine:
Do 24.10.24 10:00 - 14:00 WiWi1 A1100
|
Dr. Marten Laudi
|
Schwerpunkt "Informationstechnikmanagement"
WI-IM-WP
Auflistung der WInf-Schwerpunkt-Wahlmodule siehe unter WInf-Wahlmodule
| 07-BA37-233-01 |
Strategisches Management (in englischer Sprache)
Strategic Management
Seminar
ECTS: 6
Termine:
zweiwöchentlich (Startwoche: 2) Mi 10:00 - 14:00 GW2 B3009 (Großer Studierraum) GW1-HS H0070 GW1 B0080
|
Dr. Julia Maria Kensbock
|
| 07-BA37-233-09 |
Digital Future Challenge (in englischer Sprache)
Problem Solving for a Responsible Digital Future
Seminar
ECTS: 6
Termine:
wöchentlich Do 10:00 - 12:00 WiWi2 F3290
Einzeltermine:
Di 28.01.25 08:00 - 10:00 WiWi2 F3290
|
Prof. Dr. Benjamin Müller, MBA
|
WInf-Wahlmodule
WI-W/50 International Management
Schwerpunkte: EB, IM, LO
| 07-BA37-162-01 |
International Business (in englischer Sprache)
International Business
Vorlesung
ECTS: 6
Termine:
zweiwöchentlich (Startwoche: 2) Do 10:00 - 14:00 WiWi1 A1070
Einzeltermine:
Do 17.10.24 10:00 - 14:00 WiWi1 A1070
Do 14.11.24 10:00 - 14:00 WiWi1 A1070
Bitte beachten Sie die Veranstaltungszeiten im Ablaufplan.
|
Prof. Dr. Peter Michael Anton Bican
|
WI-W/52 Strategisches Management
Schwerpunkte: EB, IM, LO
| 07-BA37-233-01 |
Strategisches Management (in englischer Sprache)
Strategic Management
Seminar
ECTS: 6
Termine:
zweiwöchentlich (Startwoche: 2) Mi 10:00 - 14:00 GW2 B3009 (Großer Studierraum) GW1-HS H0070 GW1 B0080
|
Dr. Julia Maria Kensbock
|
