Veranstaltungsverzeichnis

Lehrveranstaltungen SoSe 2022

Fachbereich 03: Mathematik/Informatik

Veranstaltungen anzeigen: alle | in englischer Sprache | für ältere Erwachsene | mit Nachhaltigkeitszielen

Digitale Medien, B.Sc.

3. Studienjahr

B-MI-9

Auch Module aus B-MI-8 hier wählbar.
Hinweis: Studierende, die das Software-Projekt machen möchten, müssen bitte alle drei angebotenen Veranstaltungen hierzu belegen: 03-BA-901.01a (SWP1), 03-BA-901.01b (Datenbankgrundlagen) und 03-BA-901.01c (SWP Praktikum).
Bei Vorliegen der jeweiligen inhaltlichen Voraussetzungen auch: M-MI/ M-MI-d des Master
VAKTitel der VeranstaltungDozentIn
03-IBAP-CS (03-BB-711.01)Cognitive Systems (in englischer Sprache)
Grundlagen der Informationsverarbeitung in natürlichen und künstlichen Systemen

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 GW2 B1410 Vorlesung Präsenz
wöchentlich Mi 08:00 - 10:00 CART Rotunde - 0.67 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 CART Rotunde - 0.67 Übung Präsenz
Thomas Dieter Barkowsky
03-IBAP-ML (03-BB-710.10)Grundlagen des Maschinellen Lernens (in englischer Sprache)
Fundamentals of Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 3150 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 27.07.22 10:00 - 14:00 MZH 1380/1400
Mi 27.07.22 10:00 - 14:00 MZH 1470

Schwerpunkt: AI

Tanja Schultz
Felix Putze
Mazen Salous
Darius Ivucic
Gabriel Ivucic

B-MA-2

Auch Module aus den Bereichen B-MI-8 und B-MI-9 sind hier wählbar.
Für Lehrveranstaltungen dieses Moduls der Hochschule für Künste bitte das dortige Lehrveranstaltungsverzeichnis ansehen: http://www.hfk-bremen.de/t/digitale-medien
VAKTitel der VeranstaltungDozentIn
03-IBVA-DS (03-BE-802.98a)Data Science (in englischer Sprache)
Applied Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 16:00 - 18:00 Online Kurs online

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

Dr. Hendrik Heuer
Dr. Juliane Jarke

Graduiertenseminare

VAKTitel der VeranstaltungDozentIn
03-IGRAD-CoSy (03-05-H-711.91)Graduiertenseminar Cognitive Systems (in englischer Sprache)

Seminar

Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
Thomas Dieter Barkowsky

Informatica Feminale

VAKTitel der VeranstaltungDozentIn
META-2022-ALL-IF25. internationale Informatica Feminale (in englischer Sprache)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias

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.

M-MI (Media Informatics)

VAKTitel der VeranstaltungDozentIn
03-IMAA-CTHCICurrent Topics in Human Computer Interaction (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Mi 12:00 - 16:00 MZH 1470 Kurs Präsenz

Profil: DMI
Schwerpunkt: IMA-DMI, IMA-VMC

Prof. Dr. Tanja Döring
Dr. Susanne Putze
Dr. Dmitry Alexandrovsky
03-IMAA-EC (03-MB-804.03)Entertainment Computing (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 12:00 - 16:00 GW2 B1820 Vorlesung und Übung Präsenz

Profil: DMI
Schwerpunkt: IMK-DMI, IMA-VMC

Prof. Dr. Rainer Malaka
Dr. Dmitry Alexandrovsky
03-IMAP-DIS (03-MB-703.02)Design of Information Systems (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 Vorlesung online
wöchentlich Do 10:00 - 12:00 Vorlesung online

Profil: SQ.
Schwerpunkt: DMI, SQ
Die Veranstaltung ist inhaltlich identisch mit "Entwurf von Informationssystemen" (keine Doppelanerkennung möglich).

Martin Gogolla
03-IMAP-WCOMP (03-MB-799.01)Wearable Computing (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 1090
wöchentlich Mi 16:00 - 18:00 MZH 1090

Profil: KIKR, DMI.
Schwerpunkt: DMI, VMC, AI

Dipl.-Inf. Alexej Wagner
03-IMVA-EI (03-ME-899.03)Embodied Interaction (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Do 10:00 - 14:00 MZH 1450 Kurs Präsenz

Profil: DMI.
Schwerpunkt: DMI, VMC

Robert Porzel
Prof. Dr. Rainer Malaka
03-IMVA-MAD (03-ME-804.06)Mobile App Development (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 12:00 - 16:00 GW2 B1820 Vorlesung Präsenz

Profil: DMI
Schwerpunkt: DMI

Die Veranstaltung richtet sich an Studenten der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Prof. Dr. Rainer Malaka
David Ruh
Nicolas Autzen
03-IMVP-ECLEdge Computing Lab (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 16:00 - 19:00 MZH 1450 Kurs Präsenz

Schwerpunkt: AI, DMI

Peter Fereed Haddawy
Prof. Dr. Anna Förster
Thomas Dieter Barkowsky
03-IMVP-MPAR (03-ME-708.05)Massively-Parallel Algorithms (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 16:00 - 18:00 MZH 1090 MZH 1100 Vorlesung Präsenz
wöchentlich Do 08:00 - 10:00 MZH 1110 Übung Präsenz

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

Prof. Dr. Gabriel Zachmann
06-B-500.11aProgramming Autonomous Agents (in englischer Sprache)

Blockveranstaltung
ECTS: 3

Einzeltermine:
Mo 05.09.22 - Di 06.09.22 (Mo, Di) 10:00 - 17:00 Online
Mo 19.09.22 - Di 20.09.22 (Mo, Di) 10:00 - 17:00 MZH 1460
Di 27.09.22 14:00 - 16:00 CART Rotunde - 0.67

This course is for students who have already attended a programming course and are familiar with basic programming concepts such as variables, conditionals, loops, arrays, and objects. In this course, we will extend and consolidate these programming skills.

The main topic of this course are autonomous agents, e.g. programs that are "intelligent" (not really ...) and make own "decisions", such as non-player characters in computer games. Some basic concepts regarding decision-making, path planning, and swarm behaviors will be introduced. Furthermore, we might have a short look at evolutionary algorithms, which enable our programs to adapt over to time. The final of this course will be a small competition, in which our agents will compete against each other in a platform jumper.

We will program by using the Processing programming environment. The main literature will be "The Nature of Code" by Daniel Shiffman (the book is available online for free).

This block course will take place on multiple days in September. The exact dates will be announced in the course of the summer semester.

The content of this course was previously part of the "Advanced Techniques for Creative Coding in Processing" course, which is now split into two parts.

This course is not available for computer science students but it is open to Digital Media students.

Dr. Tim Laue

M-MI-d ( Media Informatics in deutscher Sprache )

VAKTitel der VeranstaltungDozentIn
03-IMS-APMSK (03-ME-711.09)Ausgewählte Probleme der multisensorischen Kognition (in englischer Sprache)
Selected Problems of Multisensory Cognition
DIE VERANSTALTUNG ENTFÄLLT

Seminar
ECTS: 3

Termine:
wöchentlich Do 12:00 - 14:00 CART Rotunde - 0.67 Seminar Präsenz

Profil: KIKR, DMI.
Die Veranstaltung findet in Englischer Sprache statt.

Christop W. Zetzsche-Schill
Kerstin Schill

M-MT (Media Theory)

Additional courses can be found at the HfK website (http://www.hfk-bremen.de/t/digitale-medien).
VAKTitel der VeranstaltungDozentIn
09-71-A.2-1Course 1: Mediatization (in englischer Sprache)

Seminar

Termine:
wöchentlich Mo 16:00 - 18:00 SH D1020 (2 SWS)


Prof. Dr. Stefanie Averbeck-Lietz
09-71-A.2-2Course 2: Datafied Society (in englischer Sprache)

Seminar

Termine:
wöchentlich Do 10:00 - 12:00 SFG 2040 SFG 1030 (2 SWS)


Dr. Sigrid Kannengießer

M-MA-2 (Special Topics in Digital Media)

All M-MI, M-MD, M-MT courses can be taken as M-MA-2
VAKTitel der VeranstaltungDozentIn
03-06-M-313Mathematics. Computer Science. Digital Media. Beginnings (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Di 14:00 - 18:00 MZH 1110 Seminar
Frieder Nake
03-IMVA-ACSSApplied Computer Science in Sports (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 6200 Übung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 21.09.22 14:00 - 18:00 CART Rotunde - 0.67

Schwerpunkt: AI, DMI
This aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.

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

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

The course will be held in English.

Schwerpunkt: AI, DMI

Robert Porzel
Dr. Tim Laue
Bastian Dänekas

M-MA-31 (Project Preparation)

VAKTitel der VeranstaltungDozentIn
03-IBVA-DS (03-BE-802.98a)Data Science (in englischer Sprache)
Applied Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 16:00 - 18:00 Online Kurs online

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

Dr. Hendrik Heuer
Dr. Juliane Jarke
03-IMS-APMSK (03-ME-711.09)Ausgewählte Probleme der multisensorischen Kognition (in englischer Sprache)
Selected Problems of Multisensory Cognition
DIE VERANSTALTUNG ENTFÄLLT

Seminar
ECTS: 3

Termine:
wöchentlich Do 12:00 - 14:00 CART Rotunde - 0.67 Seminar Präsenz

Profil: KIKR, DMI.
Die Veranstaltung findet in Englischer Sprache statt.

Christop W. Zetzsche-Schill
Kerstin Schill

2nd academic year

Graduate Seminars

VAKTitel der VeranstaltungDozentIn
03-IGRAD-CoSy (03-05-H-711.91)Graduiertenseminar Cognitive Systems (in englischer Sprache)

Seminar

Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
Thomas Dieter Barkowsky

General Studies

Concerning the language, usually you can see by the title whether a course is in English or German.
VAKTitel der VeranstaltungDozentIn
META-2022-ALL-IF25. internationale Informatica Feminale (in englischer Sprache)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias

Informatik, B.Sc./M.Sc.

Bachelor Informatik

Wahlbereich Bachelor-Aufbau (IBA) / Bachelor-Basis (BB)

IBAP / BB-7: Praktische und Technische Informatik

Nach BPO 2020 mindestens ein Lehrangebot aus dieser Kategorie wählen.
Für ,,Bachelor - PrakTechInfWahl`` zwei Module aus dieser Kategorie wählen: BB-7xx.xx. Keine Ausnahmeanträge.
VAKTitel der VeranstaltungDozentIn
03-IBAP-CS (03-BB-711.01)Cognitive Systems (in englischer Sprache)
Grundlagen der Informationsverarbeitung in natürlichen und künstlichen Systemen

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 GW2 B1410 Vorlesung Präsenz
wöchentlich Mi 08:00 - 10:00 CART Rotunde - 0.67 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 CART Rotunde - 0.67 Übung Präsenz
Thomas Dieter Barkowsky
03-IBAP-ML (03-BB-710.10)Grundlagen des Maschinellen Lernens (in englischer Sprache)
Fundamentals of Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 3150 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 27.07.22 10:00 - 14:00 MZH 1380/1400
Mi 27.07.22 10:00 - 14:00 MZH 1470

Schwerpunkt: AI

Tanja Schultz
Felix Putze
Mazen Salous
Darius Ivucic
Gabriel Ivucic
03-IBAP-MRCAModern Robot Control Architectures (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 10:00 - 12:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Vorlesung Präsenz
wöchentlich Do 14:00 - 16:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Übung Präsenz

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

This course aims to provide the fundamentals of modern robot control approaches that enable robotic agents to operate in the environment autonomously. The course introduces a basic understanding of autonomous robots, along with tools and methods to control various types of mobile robotic platforms and manipulators. Firstly, the course presents the types of sensors and actuators employed in autonomous robotic platforms. Secondly, it offers a formal understanding of the robot geometry, its kinematic and dynamic models. Finally, the course provides methods and approaches to control the robotic system from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

  • Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
  • Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
  • Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
  • Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
  • Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
  • Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
  • Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A\*).
  • Kinodynamic Planning: transcribing a dynamic planning problem into trajectory optimization, direct and indirect methods, costs and constraints.
  • Reinforcement Learning-based Control: mathematical foundations, discrete vs continuous methods, reinforcement learning for closed-loop robot control.
  • Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.
  • Optimal Control: energy-shaping control, LQR and time-varying LQR control.

Learning Outcomes

At the end of the course, the student is expected to be able to:
  • Define robot autonomy and list its key aspects.
  • Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
  • Implement and understand the low-level actuator control methods.
  • Compute the 3D world coordinate transformations for rigid bodies.
  • Apply the robot forward and inverse geometric model.
  • Describe a robotic system based on its kinematic and dynamic properties.
  • Use probabilistic methods for robot localization.
  • Generate an optimal path for a mobile robot or manipulator using graph search methods.
  • Plan a path taking into account the robot kinodynamic properties.
  • Use reinforcement learning methods to control simple robotic systems.
  • Apply dynamical and optimal control methods on robotic systems such that they are robust against disturbances.
  • Assess the strengths and limitations of different control methods presented in the course.
  • Identify open challenges in robotics research and current trends in state-of-the-art.
  • Communicate confidently using the terminology in the field of robotics.
  • Cooperate and work in teams in order to solve tasks.

Examination

a) Submission of 6 worksheets in groups of 4 students and group interview for final grade (Übungsaufgaben und Fachgespräch).
b) Individual oral exam without worksheet submission (mündliche Prüfung).

References

  • Mechanics of Robotic Manipulation, Mathew T. Masen, MIT press, 2001.
  • Algebra and Geometry, Alan F. Beardon, Cambridge University Press, 2005.
  • Modelling and Control of Robot Manipulators, Lorenzo Sciavicco, Bruno Siciliano, Springer, 2000.
  • Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press, 2005.
  • Introduction to Autonomous Mobile Robots, Siegwart R., Nourbakhsh I., Scaramuzza D., MIT press, 2011.
  • Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2004.
  • Behaviour-based robotics, R. C. Arkin, MIT press, 1998.
  • Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park, Cambridge University Press, 2017.

Frank Kirchner
M. Sc. Mihaela Popescu (Organizer)

Wahlbereich Bachelor-Vertiefung (IBV) / Bachelor-Ergänzung (BE)

Weitere Wahlangebote können aus dem Wahlbreich IBA/BB und bei Vorliegen der inhaltlichen Voraussetzungen aus dem Wahlangebot des Masterstudiengangs Informatik gewählt werden.
BPO \'10: weitere BE-Angebote unter Wahlbereich IBFW

IBVA / BE-8: Angewandte Informatik

VAKTitel der VeranstaltungDozentIn
03-IBVA-DS (03-BE-802.98a)Data Science (in englischer Sprache)
Applied Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 16:00 - 18:00 Online Kurs online

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

Dr. Hendrik Heuer
Dr. Juliane Jarke

IBFW / BE - Freie Wahl inkl. Seminare

Informationen zum Thema General Studies findet ihr auch hier: https://www.szi.uni-bremen.de/wp-content/uploads/2021/10/GSListe.pdf
VAKTitel der VeranstaltungDozentIn
03-IBFW-HTO (03-BE-699.12)Hands-on Tutorial on Optimization (in englischer Sprache)

Blockveranstaltung
ECTS: 3

Einzeltermine:
Mo 26.09.22 - Fr 30.09.22 (Mo, Di, Mi, Do, Fr) 09:00 - 17:00 MZH 6200

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

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

This course consists of two phases:

  • One week Mon-Fri (full day) of lectures and practical labs during Sep 26–30, 2022.
  • An individual project period: One project has to be modeled, implemented, and solved individually or in a group of at most two students. The topic will be either developed with or provided by the lecturers. The project including the implementation has to be presented before the beginning of the winter semester.

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

Important: please register for this course by September 1, 2022 by Email to Felix Hommelsheim (fhommels@uni-bremen.de)

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim
03-IBFW-TSTUDTeilnahme an Studien (Proband*innenstunden) (in englischer Sprache)
Participation in Studies

Seminar
ECTS: 1

Zu Beginn jedes Semesters findet eine Infoveranstaltung statt, wo die Scheinkriterien für Versuchspersonen erläutert werden. Außerdem werden (aufbauend auf den Inhalten von WA1) Forschungsmethoden von Studien mit Versuchspersonen vermittelt.
Im weiteren Verlauf des Studiums sollen Studierende 15 Versuchspersonenstunden absolvieren (d.h. an mehreren Studien teilnehmen). Jede Studienteilnahme wird mit Versuchspersonenstunden vergütet, die in ECTS anerkannt werden können. Die Versuchspersonenstunden können über mehre Semester gesammelt werden.
Die Teilnahme an den Studien soll in einer schriftlichen Ausarbeitung dokumentiert und reflektiert werden.



At the beginning of each semester, an information session is held where the certificate criteria for subjects are explained. In addition, research methods of studies with test subjects are taught (building on the contents of WA1).

Later in the program, students are expected to complete 15 subject hours (i.e., participate in different studies). Each study participation is compensated with subject hours, which can be recognized in ECTS. The subject hours may be accumulated over multiple semesters.

Participation in the studies should be documented and reflected upon in a written paper at the end.
The selection of participants is made manually after registration.

Users who wish to register for this event will receive more detailed information and can then still decide against participation.

Prof. Dr. Rainer Malaka
Prof. Dr. Tanja Döring
Dr. Susanne Putze
Dr. Dmitry Alexandrovsky

Master Informatik

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 ME-6xx.xx-Lehrangebot oder fortgeschrittenes Mathematik-Lehrangebot möglich.
VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 6200 Vorlesung Präsenz
wöchentlich Do 14:00 - 16:00 MZH 6200 Vorlesung Präsenz

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, AI, VMC
weitere Studiengänge: M-M-Alg-Num, M-T

Prof. Dr. Nicole Megow
03-IMVT-DAMDiscrete Algorithmic Methods (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 1450 Übung Präsenz
wöchentlich Di 12:00 - 14:00 GW2 B1400 NUR Mo. + Di. Vorlesung Präsenz
wöchentlich Di 12:00 - 14:00 GW2 B1410 Vorlesung Präsenz

Schwerpunkt: SQ

Prof. Dr. Kim-Manuel Klein, Dipl.-Inf

IMAP / MB-7 - Praktische und technische Informatik

Nach MPO 2020 mindestens ein Lehrangebot aus dieser Kategorie wählen. Nach MPO 2012 gilt zwei Lehrangebote aus dieser Kategorie wählen.
VAKTitel der VeranstaltungDozentIn
03-IMAP-AMLAdvanced Machine Learning (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 10:00 - 12:00 GW1 A0010 Übung Präsenz
wöchentlich Mo 14:00 - 16:00 MZH 1090 Übung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1380/1400 Vorlesung Präsenz

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC

Tanja Schultz
Lisa-Marie Vortmann, M. Sc
Felix Putze
Ayimnisagul Ablimit
Daniel Reich
Marvin Borsdorf, M.Sc.
Abdul Haq Azeem Paracha
03-IMAP-DIS (03-MB-703.02)Design of Information Systems (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 Vorlesung online
wöchentlich Do 10:00 - 12:00 Vorlesung online

Profil: SQ.
Schwerpunkt: DMI, SQ
Die Veranstaltung ist inhaltlich identisch mit "Entwurf von Informationssystemen" (keine Doppelanerkennung möglich).

Martin Gogolla
03-IMAP-RL (03-ME-712.03)Reinforcement Learning (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 12:00 - 14:00 DFKI RH1 B0.10 Kurs Präsenz
wöchentlich Do 16:00 - 18:00 DFKI RH1 B0.10 Kurs Präsenz

Profil: KIKR.
Schwerpunkt: IMA-AI, VMC

Frank Kirchner
Melvin Laux
03-IMAP-WCOMP (03-MB-799.01)Wearable Computing (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 1090
wöchentlich Mi 16:00 - 18:00 MZH 1090

Profil: KIKR, DMI.
Schwerpunkt: DMI, VMC, AI

Dipl.-Inf. Alexej Wagner

IMAA / MB-8 - Angewandte Informatik

Nach MPO 2012 gilt ein Lehrangebot aus dieser Kategorie wählen. Nur nach MPO 2012 auf Antrag auch ME-8xx.xx-Modul möglich.
VAKTitel der VeranstaltungDozentIn
03-IMAA-CTHCICurrent Topics in Human Computer Interaction (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Mi 12:00 - 16:00 MZH 1470 Kurs Präsenz

Profil: DMI
Schwerpunkt: IMA-DMI, IMA-VMC

Prof. Dr. Tanja Döring
Dr. Susanne Putze
Dr. Dmitry Alexandrovsky
03-IMAA-EC (03-MB-804.03)Entertainment Computing (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 12:00 - 16:00 GW2 B1820 Vorlesung und Übung Präsenz

Profil: DMI
Schwerpunkt: IMK-DMI, IMA-VMC

Prof. Dr. Rainer Malaka
Dr. Dmitry Alexandrovsky

Wahlbereich Master-Vertiefung (IMV) / Master-Ergänzung (ME)

MPO 2012: weitere ME-Angebote unter Wahlbereich IMS/ME und unter General Studies IMGS

IMVT / ME-6 - Theoretische Informatik

VAKTitel der VeranstaltungDozentIn
03-IMVT-DGADatabases, graphs and algorithms (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 08:00 - 10:00 Online Vorlesung online
wöchentlich Do 08:00 - 10:00 Online ÜbungOnline

Profil: SQ
Schwerpunkt: SQ
Die Veranstaltung findet online statt.

Alexandre Vigny
03-M-WP-62Discrete Optimization (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2490 (Seminarraum) Vorlesung Präsenz
wöchentlich Mi 12:00 - 14:00 MZH 4140 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 2490 (Seminarraum)

The development of solving techniques for linear programming tasks are a huge contribution of Mathematics to the solution of practical optimization problems. This course is an introduction both into the theory and the application of linear and integer optimization. We will cover the theoretical background as well as algorithmic ideas and practical applications. Main topics that we will cover in the course are the Simplex Method, Ellipsoid Method, Interior Point Method, Cutting Planes, Branch & Bound, LP Duality, and Polyhedral Theory.

Prof. Dr. Daniel Schmand

IMVP / ME-7 - Praktische Informatik

VAKTitel der VeranstaltungDozentIn
03-IMVP-ECLEdge Computing Lab (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 16:00 - 19:00 MZH 1450 Kurs Präsenz

Schwerpunkt: AI, DMI

Peter Fereed Haddawy
Prof. Dr. Anna Förster
Thomas Dieter Barkowsky
03-IMVP-HCIR (03-ME-712.04)Human-Centered Interaction in Robotics (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Übung Präsenz

Profil: KIKR
Schwerpunkt: AI

Frank Kirchner
Dr. rer. nat. Teena Hassan
03-IMVP-MPAR (03-ME-708.05)Massively-Parallel Algorithms (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 16:00 - 18:00 MZH 1090 MZH 1100 Vorlesung Präsenz
wöchentlich Do 08:00 - 10:00 MZH 1110 Übung Präsenz

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

Prof. Dr. Gabriel Zachmann
03-IMVP-ROSD (03-ME-702.04)Real-time Operating Systems Development (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 1110 Vorlesung Präsenz
wöchentlich Mi 10:00 - 12:00 MZH 1110 Übung Präsenz

Profil: SQ
Schwerpunkt: SQ

Prof. Dr. Jan Peleska
03-IMVP-SES (03-ME-702.03)Specification of Embedded Systems (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 1450 Kurs Präsenz
wöchentlich Di 14:00 - 16:00 MZH 1450 Kurs Präsenz

Profil: SQ
Schwerpunkt: SQ

Prof. Dr. Jan Peleska
Dr. Robert Sachtleben
03-IMVP-VHSVerification of Hybrid Systems (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 12:00 - 14:00 MZH 3150 Vorlesung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 3150 Übung Präsenz

Schwerpunkt: SQ

Dr. Mario Gleirscher

IMVA / ME-8 - Angewandte Informatik

VAKTitel der VeranstaltungDozentIn
03-IMVA-ACSSApplied Computer Science in Sports (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 6200 Übung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 21.09.22 14:00 - 18:00 CART Rotunde - 0.67

Schwerpunkt: AI, DMI
This aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.

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

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

The course will be held in English.

Schwerpunkt: AI, DMI

Robert Porzel
Dr. Tim Laue
Bastian Dänekas
03-IMVA-EI (03-ME-899.03)Embodied Interaction (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Do 10:00 - 14:00 MZH 1450 Kurs Präsenz

Profil: DMI.
Schwerpunkt: DMI, VMC

Robert Porzel
Prof. Dr. Rainer Malaka
03-IMVA-MAD (03-ME-804.06)Mobile App Development (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 12:00 - 16:00 GW2 B1820 Vorlesung Präsenz

Profil: DMI
Schwerpunkt: DMI

Die Veranstaltung richtet sich an Studenten der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

Prof. Dr. Rainer Malaka
David Ruh
Nicolas Autzen

Wahlbereich IMS / ME - Master Seminare

VAKTitel der VeranstaltungDozentIn
03-IMS-AISSeminar on Autonomous and Intelligent Systems (in englischer Sprache)

Seminar
ECTS: 3

Termine:
wöchentlich Di 16:00 - 18:00 DFKI RH1 B0.10 Seminar Präsenz

Einzeltermine:
Di 19.07.22 14:00 - 18:00 RH1 B0.10

Profil: KIKR

Frank Kirchner
Melvin Laux
03-IMS-APMSK (03-ME-711.09)Ausgewählte Probleme der multisensorischen Kognition (in englischer Sprache)
Selected Problems of Multisensory Cognition
DIE VERANSTALTUNG ENTFÄLLT

Seminar
ECTS: 3

Termine:
wöchentlich Do 12:00 - 14:00 CART Rotunde - 0.67 Seminar Präsenz

Profil: KIKR, DMI.
Die Veranstaltung findet in Englischer Sprache statt.

Christop W. Zetzsche-Schill
Kerstin Schill

General Studies

Informationen zum Thema General Studies findet ihr auch hier: https://www.szi.uni-bremen.de/wp-content/uploads/2021/10/GSListe.pdf

Veranstaltungen aus anderen Studiengängen (Auswahl)

VAKTitel der VeranstaltungDozentIn
03-06-M-313Mathematics. Computer Science. Digital Media. Beginnings (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Di 14:00 - 18:00 MZH 1110 Seminar
Frieder Nake
04-M30-CEM-SFI-1On Board Data Handling (in englischer Sprache)

Vorlesung
ECTS: 3

Termine:
wöchentlich Do 10:00 - 12:00 IW3 0200
Dr. rer. nat. Frank Dannemann

Graduiertenseminare

VAKTitel der VeranstaltungDozentIn
03-05-H-711.90SFB/IQN-Colloquium Spatial Cognition (in englischer Sprache)

Colloquium

Termine:
wöchentlich Fr 15:15 - 16:45 Kolloquium
Prof. John Arnold Bateman, Ph.D.
Kerstin Schill
03-IGRAD-CoSy (03-05-H-711.91)Graduiertenseminar Cognitive Systems (in englischer Sprache)

Seminar

Termine:
zweiwöchentlich (Startwoche: 16) Mi 14:00 - 17:00 Graduiertenseminar
Thomas Dieter Barkowsky

Veranstaltungen für andere Studiengänge

VAKTitel der VeranstaltungDozentIn
META-2022-ALL-IF25. internationale Informatica Feminale (in englischer Sprache)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias

Sonstige Veranstaltungen ohne Kreditpunkte

VAKTitel der VeranstaltungDozentIn
03-D-800.01EDM 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

Informatica Feminale

Deutschlandweites Sommerstudium fuer Frauen in der Informatik.

Die Informatica Feminale bietet jaehrlich kompakte Lehre zur Informatik fuer Studentinnen aller Hochschularten und fuer an Weiterbildung interessierte Frauen. Studieneinstieg, Verbleib im Studium, Berufsuebergang und lebenslanges Lernen auf universitaerem Niveau stehen dabei gleichermassen im Blickfeld. Dozentinnen und Teilehmerinnen kommen aus dem In- und Ausland. Das Sommerstudium in der Universitaet Bremen ist ein Ort des Experimentierens, um neue Konzepte fuer das Informatikstudium zu finden.

Alle Informationen unter: http://www.informatica-feminale.de/
VAKTitel der VeranstaltungDozentIn
META-2022-ALL-IF25. internationale Informatica Feminale (in englischer Sprache)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias

Mathematik, B.Sc./M.Sc. (Studienbeginn vor 2022)

Bachelor: Proseminare

VAKTitel der VeranstaltungDozentIn
03-M-PS-23Statistical Programming with R (in englischer Sprache)

Proseminar
ECTS: 5 / 6

Termine:
wöchentlich Do 12:00 - 14:00 Vorlesung
Maryam Movahedifar

Master: Wahlpflichtveranstaltungen

Vertiefungsrichtung Algebra

VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 6200 Vorlesung Präsenz
wöchentlich Do 14:00 - 16:00 MZH 6200 Vorlesung Präsenz

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, AI, VMC
weitere Studiengänge: M-M-Alg-Num, M-T

Prof. Dr. Nicole Megow
03-M-WP-63Applied Algebraic Topology (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 7200 Vorlesung
wöchentlich Mo 12:00 - 14:00 MZH 7200 Übung
wöchentlich Do 10:00 - 12:00 MZH 4140 Vorlesung
Anastasios Stefanou

Vertiefungsrichtung Analysis

VAKTitel der VeranstaltungDozentIn
03-M-WP-51Mathematische Grundlagen des maschinellen Lernens (in englischer Sprache)
Mathematical Foundations of Machine Learning

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1100 Vorlesung Präsenz
wöchentlich Fr 08:00 - 10:00 MZH 4140 Übung Präsenz
wöchentlich Fr 10:00 - 12:00 MZH 4140 Vorlesung Präsenz
Peter Maaß
Dr. Matthias Beckmann
03-M-WP-60Applied Asymptotic Analysis (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 16:00 - 18:00 MZH 1110 Vorlesung Präsenz
wöchentlich Fr 12:00 - 14:00 MZH 4140 Vorlesung Präsenz
wöchentlich Fr 14:00 - 16:00 MZH 4140 Übung Präsenz
Bingying Lu

Vertiefungsrichtung Numerik

VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 6200 Vorlesung Präsenz
wöchentlich Do 14:00 - 16:00 MZH 6200 Vorlesung Präsenz

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, AI, VMC
weitere Studiengänge: M-M-Alg-Num, M-T

Prof. Dr. Nicole Megow
03-M-WP-51Mathematische Grundlagen des maschinellen Lernens (in englischer Sprache)
Mathematical Foundations of Machine Learning

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1100 Vorlesung Präsenz
wöchentlich Fr 08:00 - 10:00 MZH 4140 Übung Präsenz
wöchentlich Fr 10:00 - 12:00 MZH 4140 Vorlesung Präsenz
Peter Maaß
Dr. Matthias Beckmann
03-M-WP-62Discrete Optimization (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2490 (Seminarraum) Vorlesung Präsenz
wöchentlich Mi 12:00 - 14:00 MZH 4140 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 2490 (Seminarraum)

The development of solving techniques for linear programming tasks are a huge contribution of Mathematics to the solution of practical optimization problems. This course is an introduction both into the theory and the application of linear and integer optimization. We will cover the theoretical background as well as algorithmic ideas and practical applications. Main topics that we will cover in the course are the Simplex Method, Ellipsoid Method, Interior Point Method, Cutting Planes, Branch & Bound, LP Duality, and Polyhedral Theory.

Prof. Dr. Daniel Schmand

Vertiefungsrichtung Stochastik & Statistik

VAKTitel der VeranstaltungDozentIn
03-M-WP-66Time Series Analysis II (in englischer Sprache)
Time Series Analysis II: Frequency Domain Analysis
Frequency Domain Analysis

Vorlesung
ECTS: 4,5

Termine:
wöchentlich Di 10:00 - 12:00 Vorlesung
zweiwöchentlich (Startwoche: 1) Di 12:00 - 14:00 Übung
Maryam Movahedifar

Master: Seminare

Vertiefungsrichtung Algebra

VAKTitel der VeranstaltungDozentIn
03-M-SEM-34Computational Algebra (in englischer Sprache)

Seminar
ECTS: 6 (5)

Termine:
wöchentlich Do 12:00 - 14:00 MZH 7200 Seminar
Anastasios Stefanou

Vertiefungsrichtung Stochastik & Statistik

VAKTitel der VeranstaltungDozentIn
03-M-PS-23Statistical Programming with R (in englischer Sprache)

Proseminar
ECTS: 5 / 6

Termine:
wöchentlich Do 12:00 - 14:00 Vorlesung
Maryam Movahedifar

Oberseminare

VAKTitel der VeranstaltungDozentIn
03-M-OS-7Oberseminar Mathematical Parameter Identification (RTG-Seminar) (in englischer Sprache)
Research Seminar - Mathematical Parameter Identification

Seminar


Tobias Kluth
Daniel Otero Baguer

General Studies

VAKTitel der VeranstaltungDozentIn
03-M-GS-9Workshop on Stata (in englischer Sprache)

Seminar
ECTS: 2

Einzeltermine:
Do 30.06.22 - Sa 02.07.22 (Do, Fr, Sa) 09:00 - 17:00

Note: The course will be held online.

Target group: Students with basic knowledge of statistics (For e.g.: understanding on normal distribution, t-test, chi-square test, p-value)

Course description: Students will be introduced to performing data management and simple to intermediate statistical analyses using Stata. The students will be using practical examples on how datasets and analyses should be described and documented in order to ensure the reproducibility of their own research.

Precourse preparation: Students are required to acquire short-term (1 – week) license request using the following link
https://www.stata.com/customer-service/short-term-license/
Note: Please apply for license only 2-3 days before the course as the license is valid only for 7 days.

Course content:
 Navigating the Stata interface
 Creating, importing and exporting datasets
 Structure of Stata dataset
 Elements of Stata syntax
 Creating and maintaining do files
 Saving work in log file
 Data management
 Modifying datasets
 Summarizing data
 Graphics and data visualization
 Statistical analysis
 Looping on repeated tasks

Exam and assessment: Students will be assessed via assigned statistical tasks for preparation of do-and log files at the end of the course.

Rajini Nagrani
03-M-GS-10Scientific Programming (in englischer Sprache)
an introduction with case studies

Vorlesung
ECTS: 3

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) (2 SWS) Projekt virtuell

Einzeltermine:
Mi 01.06.22 15:15 - 16:45 Zoom

Research software development deserves a systematic approach to keep up with the demand for reproducible science and reuse of codes as a citeable scientific output.
In the context of Open Science, sustainable research software is more and more estimated as vital component of research infrastructures.
This course provides an introduction to the practice of scientific programming to a broader audience within the General Studies. The basis are real world research codes that will be explored and executed on local programming environments - either on students laptops or on central compute nodes at University Bremen.
Principles of code-management and code publication will be actively explored in small practical projects, open for students interests in bringing their own software projects. Two main "code use cases" are provided, based on Fortran and C programming language, and further projects are offered for R and C++ as well. Special emphasis is laid on performance optimization and two standard approaches of parallelization, i.e. loop parallelization and domain decomposition.

The course is useful for math students and interested participants from other fields (e.g. industrial math, numerics of PDEs, modeling seminar, material sciences - ProMat).

Prof. Dr. Stephan Frickenhaus
SZHB 0636English for Mathematicians and Industrial Mathematicians (B2.3) (in englischer Sprache)
Eingangsniveau: B2.2

Kurs
ECTS: 3

Termine:
wöchentlich Di 16:15 - 17:45 GW2 A4270 (CIP-Raum FZHB) (2 SWS)


Valerie Scholes

Medical Biometry / Biostatistics, M.Sc.

2. Modulbereich Anwendungsfelder und biomedizinische Grundlagen

VAKTitel der VeranstaltungDozentIn
03-8B53Biometric Research in Medicine (in englischer Sprache)
Probleme aus der biometrischen Forschung (Medizin)

Seminar
ECTS: 2

Termine:
wöchentlich Mi 10:00 - 12:00

The course is again organised as an online-only seminar, as some of you are not yet in Germany and it is the only course in the next semester, which gives you the opportunity to work at the place where your Master's thesis is supervised.

Details:
An oral presentation is given which approaches the topic of the master thesis systematically:
i. General overview over the medical and methodological problem
ii. Narrowing the topic to a relevant core
iii. Approach and working program for the work on the problem

Please register at Stud.IP for the course and sign in to this table

https://1drv.ms/x/s!AggYJ8v5lxnKjSDY7wTSypaiVf6-?e=D2FoCT

for a date which suits you and your supervisor: one row per person, i.e. two presentations per lesson (~30 min presentation + 15 minutes discussion). Please make sure that your supervisor takes part at the seminar when you present about your topic. The first date is blocked for an introduction in how to write and structure a Master's Thesis.

The zoom link for the whole semester is: https://uni-bremen.zoom.us/j/98959236333?pwd=VGdwRWtjcmY0Q202a1ZmaWZrYVFodz09

Helpful link to regarding plagiarism (see also the checklist for citation etiquette on that page):

https://ethz.ch/students/en/studies/performance-assessments/plagiarism.html

Prof. Dr. Iris Pigeot-Kübler

3. Wahlbereich

VAKTitel der VeranstaltungDozentIn
03-M-GS-9Workshop on Stata (in englischer Sprache)

Seminar
ECTS: 2

Einzeltermine:
Do 30.06.22 - Sa 02.07.22 (Do, Fr, Sa) 09:00 - 17:00

Note: The course will be held online.

Target group: Students with basic knowledge of statistics (For e.g.: understanding on normal distribution, t-test, chi-square test, p-value)

Course description: Students will be introduced to performing data management and simple to intermediate statistical analyses using Stata. The students will be using practical examples on how datasets and analyses should be described and documented in order to ensure the reproducibility of their own research.

Precourse preparation: Students are required to acquire short-term (1 – week) license request using the following link
https://www.stata.com/customer-service/short-term-license/
Note: Please apply for license only 2-3 days before the course as the license is valid only for 7 days.

Course content:
 Navigating the Stata interface
 Creating, importing and exporting datasets
 Structure of Stata dataset
 Elements of Stata syntax
 Creating and maintaining do files
 Saving work in log file
 Data management
 Modifying datasets
 Summarizing data
 Graphics and data visualization
 Statistical analysis
 Looping on repeated tasks

Exam and assessment: Students will be assessed via assigned statistical tasks for preparation of do-and log files at the end of the course.

Rajini Nagrani

4. Sonstige Veranstaltungen

VAKTitel der VeranstaltungDozentIn
03-M-GS-9Workshop on Stata (in englischer Sprache)

Seminar
ECTS: 2

Einzeltermine:
Do 30.06.22 - Sa 02.07.22 (Do, Fr, Sa) 09:00 - 17:00

Note: The course will be held online.

Target group: Students with basic knowledge of statistics (For e.g.: understanding on normal distribution, t-test, chi-square test, p-value)

Course description: Students will be introduced to performing data management and simple to intermediate statistical analyses using Stata. The students will be using practical examples on how datasets and analyses should be described and documented in order to ensure the reproducibility of their own research.

Precourse preparation: Students are required to acquire short-term (1 – week) license request using the following link
https://www.stata.com/customer-service/short-term-license/
Note: Please apply for license only 2-3 days before the course as the license is valid only for 7 days.

Course content:
 Navigating the Stata interface
 Creating, importing and exporting datasets
 Structure of Stata dataset
 Elements of Stata syntax
 Creating and maintaining do files
 Saving work in log file
 Data management
 Modifying datasets
 Summarizing data
 Graphics and data visualization
 Statistical analysis
 Looping on repeated tasks

Exam and assessment: Students will be assessed via assigned statistical tasks for preparation of do-and log files at the end of the course.

Rajini Nagrani

Technomathematik, B.Sc./M.Sc. (Studienbeginn vor 2022)

Master: Wahlpflichtveranstaltungen

VAKTitel der VeranstaltungDozentIn
03-IMAT-APX (03-ME-602.99a)Approximation Algorithms (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 08:00 - 10:00 MZH 6200 Vorlesung Präsenz
wöchentlich Do 14:00 - 16:00 MZH 6200 Vorlesung Präsenz

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, AI, VMC
weitere Studiengänge: M-M-Alg-Num, M-T

Prof. Dr. Nicole Megow
03-M-WP-51Mathematische Grundlagen des maschinellen Lernens (in englischer Sprache)
Mathematical Foundations of Machine Learning

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 10:00 - 12:00 MZH 1100 Vorlesung Präsenz
wöchentlich Fr 08:00 - 10:00 MZH 4140 Übung Präsenz
wöchentlich Fr 10:00 - 12:00 MZH 4140 Vorlesung Präsenz
Peter Maaß
Dr. Matthias Beckmann
03-M-WP-60Applied Asymptotic Analysis (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mi 16:00 - 18:00 MZH 1110 Vorlesung Präsenz
wöchentlich Fr 12:00 - 14:00 MZH 4140 Vorlesung Präsenz
wöchentlich Fr 14:00 - 16:00 MZH 4140 Übung Präsenz
Bingying Lu
03-M-WP-62Discrete Optimization (in englischer Sprache)

Vorlesung
ECTS: 9

Termine:
wöchentlich Mo 10:00 - 12:00 MZH 2490 (Seminarraum) Vorlesung Präsenz
wöchentlich Mi 12:00 - 14:00 MZH 4140 Vorlesung Präsenz
wöchentlich Mi 16:00 - 18:00 MZH 2490 (Seminarraum)

The development of solving techniques for linear programming tasks are a huge contribution of Mathematics to the solution of practical optimization problems. This course is an introduction both into the theory and the application of linear and integer optimization. We will cover the theoretical background as well as algorithmic ideas and practical applications. Main topics that we will cover in the course are the Simplex Method, Ellipsoid Method, Interior Point Method, Cutting Planes, Branch & Bound, LP Duality, and Polyhedral Theory.

Prof. Dr. Daniel Schmand

Oberseminare

VAKTitel der VeranstaltungDozentIn
03-M-OS-7Oberseminar Mathematical Parameter Identification (RTG-Seminar) (in englischer Sprache)
Research Seminar - Mathematical Parameter Identification

Seminar


Tobias Kluth
Daniel Otero Baguer

General Studies

VAKTitel der VeranstaltungDozentIn
03-M-GS-9Workshop on Stata (in englischer Sprache)

Seminar
ECTS: 2

Einzeltermine:
Do 30.06.22 - Sa 02.07.22 (Do, Fr, Sa) 09:00 - 17:00

Note: The course will be held online.

Target group: Students with basic knowledge of statistics (For e.g.: understanding on normal distribution, t-test, chi-square test, p-value)

Course description: Students will be introduced to performing data management and simple to intermediate statistical analyses using Stata. The students will be using practical examples on how datasets and analyses should be described and documented in order to ensure the reproducibility of their own research.

Precourse preparation: Students are required to acquire short-term (1 – week) license request using the following link
https://www.stata.com/customer-service/short-term-license/
Note: Please apply for license only 2-3 days before the course as the license is valid only for 7 days.

Course content:
 Navigating the Stata interface
 Creating, importing and exporting datasets
 Structure of Stata dataset
 Elements of Stata syntax
 Creating and maintaining do files
 Saving work in log file
 Data management
 Modifying datasets
 Summarizing data
 Graphics and data visualization
 Statistical analysis
 Looping on repeated tasks

Exam and assessment: Students will be assessed via assigned statistical tasks for preparation of do-and log files at the end of the course.

Rajini Nagrani
03-M-GS-10Scientific Programming (in englischer Sprache)
an introduction with case studies

Vorlesung
ECTS: 3

Termine:
wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) (2 SWS) Projekt virtuell

Einzeltermine:
Mi 01.06.22 15:15 - 16:45 Zoom

Research software development deserves a systematic approach to keep up with the demand for reproducible science and reuse of codes as a citeable scientific output.
In the context of Open Science, sustainable research software is more and more estimated as vital component of research infrastructures.
This course provides an introduction to the practice of scientific programming to a broader audience within the General Studies. The basis are real world research codes that will be explored and executed on local programming environments - either on students laptops or on central compute nodes at University Bremen.
Principles of code-management and code publication will be actively explored in small practical projects, open for students interests in bringing their own software projects. Two main "code use cases" are provided, based on Fortran and C programming language, and further projects are offered for R and C++ as well. Special emphasis is laid on performance optimization and two standard approaches of parallelization, i.e. loop parallelization and domain decomposition.

The course is useful for math students and interested participants from other fields (e.g. industrial math, numerics of PDEs, modeling seminar, material sciences - ProMat).

Prof. Dr. Stephan Frickenhaus
SZHB 0636English for Mathematicians and Industrial Mathematicians (B2.3) (in englischer Sprache)
Eingangsniveau: B2.2

Kurs
ECTS: 3

Termine:
wöchentlich Di 16:15 - 17:45 GW2 A4270 (CIP-Raum FZHB) (2 SWS)


Valerie Scholes

Systems Engineering, B.Sc. / M.Sc.

VAKTitel der VeranstaltungDozentIn
03-IBAP-ML (03-BB-710.10)Grundlagen des Maschinellen Lernens (in englischer Sprache)
Fundamentals of Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 3150 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 27.07.22 10:00 - 14:00 MZH 1380/1400
Mi 27.07.22 10:00 - 14:00 MZH 1470

Schwerpunkt: AI

Tanja Schultz
Felix Putze
Mazen Salous
Darius Ivucic
Gabriel Ivucic
03-IMAP-AMLAdvanced Machine Learning (in englischer Sprache)

Vorlesung
ECTS: 6

Termine:
wöchentlich Mo 10:00 - 12:00 GW1 A0010 Übung Präsenz
wöchentlich Mo 14:00 - 16:00 MZH 1090 Übung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1380/1400 Vorlesung Präsenz

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC

Tanja Schultz
Lisa-Marie Vortmann, M. Sc
Felix Putze
Ayimnisagul Ablimit
Daniel Reich
Marvin Borsdorf, M.Sc.
Abdul Haq Azeem Paracha
03-IMAP-RL (03-ME-712.03)Reinforcement Learning (in englischer Sprache)

Kurs
ECTS: 6

Termine:
wöchentlich Di 12:00 - 14:00 DFKI RH1 B0.10 Kurs Präsenz
wöchentlich Do 16:00 - 18:00 DFKI RH1 B0.10 Kurs Präsenz

Profil: KIKR.
Schwerpunkt: IMA-AI, VMC

Frank Kirchner
Melvin Laux

Wirtschaftsinformatik, B.Sc.

2./3. Studienjahr

Wahlmodule

Schwerpunkt "Computational Finance"

WI-CF-WP

Auflistung der WInf-Schwerpunkt-Wahlmodule siehe unter WInf-Wahlmodule
VAKTitel der VeranstaltungDozentIn
03-IBAP-ML (03-BB-710.10)Grundlagen des Maschinellen Lernens (in englischer Sprache)
Fundamentals of Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 14:00 - 16:00 MZH 3150 Übung Präsenz
wöchentlich Mi 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz
wöchentlich Mi 14:00 - 16:00 MZH 1100 Übung Präsenz

Einzeltermine:
Mi 27.07.22 10:00 - 14:00 MZH 1380/1400
Mi 27.07.22 10:00 - 14:00 MZH 1470

Schwerpunkt: AI

Tanja Schultz
Felix Putze
Mazen Salous
Darius Ivucic
Gabriel Ivucic

Schwerpunkt "E-Business"

WI-EB-P

VAKTitel der VeranstaltungDozentIn
07-B37-4-13-15Digital Business and Management (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Di 14:00 - 16:00 SFG 1020
Dr. Julia Maria Kensbock

WInf-Wahlmodule

WI-W/11 Data Science

Schwerpunkt: CF
VAKTitel der VeranstaltungDozentIn
03-IBVA-DS (03-BE-802.98a)Data Science (in englischer Sprache)
Applied Machine Learning

Kurs
ECTS: 6

Termine:
wöchentlich Mo 16:00 - 18:00 Online Kurs online

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

Dr. Hendrik Heuer
Dr. Juliane Jarke

WI-W/50 International Management

Schwerpunkte: EB, IM, LO
VAKTitel der VeranstaltungDozentIn
07-B37-4-13-01International Management (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Mo 12:00 - 14:00 WiWi1 A1100


Prof. Dr. Sarianna Maarit Lundan

WI-W/67 Digital Ethics

Schwerpunkte: EB, IM
VAKTitel der VeranstaltungDozentIn
07-B37-4-13-16Digital Ethics (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Do 08:00 - 10:00 WiWi1 A1100

In the summer of 2020, a series of antitrust hearings in the US—involving some of the world’s leading tech companies—highlighted the double-edged nature of many emerging digital technologies, including artificial intelligence (AI), the Internet of Things (IoT), blockchain and big data. While advanced digital technologies are often linked to exciting opportunities for organizational transformation and societal growth, we are beginning to realize that these technologies also come with potentially undesirable impacts. The hearings covered a range of contentious topics, including the regulation of free speech, data privacy and AI bias, emphasizing the fact that the ability to identify, analyze, and potentially mitigate ethical tensions in a digital context is a key skill in the digital economy we are currently building. Indeed, as humans weave modern digital technologies so tightly into the fabric of everyday life, enhancing their capabilities with cutting edge technologies, the need for scrutiny and safeguards becomes paramount if progress in not only to be driven by what is technologically possible, but by what is societally desirable and sustainable.

One way for organizations to minimize the ethical risks associated with new digital technologies is to put in place policies that encourage a responsible approach to their development, use and modification. While such policies should be considered as part of an organization’s larger corporate responsibility, this seminar will make a case that the history and nature of digital technologies warrant an explicit and—to some degree—separate consideration of digital ethics in business and society.

Topics we will discuss during this seminar include:
> Historical and conceptual roots of digital ethics
> Digital issues in the context of corporate responsibility
> Foundational frameworks of corporate digital responsibility
> Design and implementation of corporate digital responsibility

Overall, the seminar is designed to allow you to understand key concepts that currently shape discussions of digital ethics while building up the skills needed to apply these concepts to specific contexts, cases, and challenges you will face.

Completing this course will support you in developing the following skills:
> Explain sources of ethical issues in a digital context
> Anticipate and analyze ethical issues in a digital context
> Understand foundational frameworks of corporate digital responsibility
> Apply foundational frameworks to industry cases

Benjamin Müller

WI-W/69 Digital Business and Management

VAKTitel der VeranstaltungDozentIn
07-B37-4-13-15Digital Business and Management (in englischer Sprache)

Seminar
ECTS: 6

Termine:
wöchentlich Di 14:00 - 16:00 SFG 1020
Dr. Julia Maria Kensbock

General Studies

VAKTitel der VeranstaltungDozentIn
META-2022-ALL-IF25. internationale Informatica Feminale (in englischer Sprache)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias

Weitere Veranstaltungen

VAKTitel der VeranstaltungDozentIn
07-B37-4-33-01PRAXIS Summer Camp: Praxis hautnah erleben - Kleinprojekte mit Unternehmen (in englischer Sprache)
Experience first hand practice - small projects with companies

Seminar
ECTS: 6

Einzeltermine:
Do 21.04.22 16:00 - 18:00 DIGITAL
Fr 13.05.22 14:00 - 16:00 WiWi1 A1100
Mo 16.05.22 14:00 - 16:00 DIGITAL
Fr 20.05.22 14:00 - 18:00 DIGITAL
Do 04.08.22 17:00 - 19:00 WiWi1 A1100
Mo 08.08.22 09:00 - 16:00
Di 09.08.22 09:00 - 16:00 SFG 2040
Di 09.08.22 09:00 - 18:00 WiWi1 A1020
Mi 10.08.22 09:00 - 16:00 SFG 2040
Mi 10.08.22 - Do 11.08.22 (Mi, Do) 09:00 - 18:00 WiWi1 A1020
Do 11.08.22 09:00 - 16:00 SFG 2040
Fr 12.08.22 09:00 - 18:00 WiWi1 A1020
Fr 12.08.22 09:00 - 16:00 SFG 2040
Mo 15.08.22 09:00 - 16:00 SFG 2040
Mo 15.08.22 - Di 16.08.22 (Mo, Di) 09:00 - 18:00 WiWi1 A1020
Di 16.08.22 - Mi 17.08.22 (Di, Mi) 09:00 - 16:00 SFG 2040
Mi 17.08.22 - Do 18.08.22 (Mi, Do) 09:00 - 18:00 WiWi1 A1020
Do 18.08.22 - Fr 19.08.22 (Do, Fr) 09:00 - 16:00 SFG 2040
Fr 19.08.22 09:00 - 18:00 WiWi1 A1020
Mo 22.08.22 09:00 - 16:00 SFG 2040
Mo 22.08.22 - Di 23.08.22 (Mo, Di) 09:00 - 18:00 WiWi1 A1020
Di 23.08.22 - Mi 24.08.22 (Di, Mi) 09:00 - 16:00 SFG 2040
Mi 24.08.22 09:00 - 18:00 WiWi1 A1020
Do 25.08.22 09:00 - 16:00 SFG 2040
Do 25.08.22 09:00 - 18:00 WiWi1 A1020
Fr 26.08.22 09:00 - 16:00 SFG 2040


Dipl.-Oec. Maren Hartstock