Students attending a laboratory courses may only do so after joining in the safety training with fire drill. Participation in the one-time exercise is therefore a compulsory event for all students concerned.

Update from 24.09.2021
According to the university' s corona regulations, the event will consider the 3G rule. Therefore, participation is only possible with a corresponding proof - vaccinated, tested or recovered - which must be shown at the entrance to the building. Students who need a test, please do it one day before.

The entrance starts one hour before the event begins. Please be there on time for the start at 8 o'clock.

The following fire drill will take place at the practice area in the Emmy-Noether-Strasse behind the SFG building in groups of no more than 100 people. The group formation will take place in the lecture hall after the presentation. Because the exercise will be outside, please remember to wear weatherproof shoes and clothing.

Detailed information about the current Corona hygiene and safety rules at the University of Bremen can be seen under the following link: https://www.uni-bremen.de/informationen-zur-corona-pandemie/fragen-antworten/informationen-fuer-studierende-und-studienanfaengerinnen

Please check StudIp again shortly before the event for the latest news.

Dieser Kurs knüpft an das Modul Digitale Signalverarbeitung an und behandelt fortgeschrittene Themen der digitalen Signalverarbeitung wie die diskrete Fouriertransformation (DFT), ihre Anwendungen, lineare Schätzprobleme und die Spektralschätzung.

The date for the initial organisational meeting of this lab, including grouping, will be advertised shortly. Room NW1 N1250.

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

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

Anzahl der Teilnehmer ist begrenzt.

Block-Course

Elective course for M.Sc. CIT, Electrical Engineering & Information Technology and Space-ST.

Contents:
• Why swarm exploration – some motivational examples with applications in space and on Earth
• Preliminaries: selected mathematical formalism (vector spaces, matrices, representation and approximations in vector spaces, least squares, convex optimization)
• Recap of probability and statistics (calculus of probabilities, moments, Bayesian theory)
• Machine learning tools (supervised learning, linear regression, kernel methods, neural networks, impact of regularization, sparsity and compressed sensing)
• Models for multi-agent networks (connected network models, distributed inference strategies)
• Distributed machine learning and exploration (models for static spatial regression, information-theoretic exploration approaches, Bayesian sequential methods for learning and exploration)
• Discussion of several practical examples of swarm exploration solutions (cooperative localization, information-driven sparse mapping of magnetic fields, exploration of sparse gas sources using a swarm of mobile robots)
As outcome, the students should be able to:
• Understand key concepts in distributed information processing over networks,
• Explain and apply mathematical tools needed to implement classical machine learning algorithms in distributed settings

The students will learn about various research fields and applications of communication networks, which target the sustainable development goals (SDG) of the United Nations. The students will individually explore a given topic (with the help of research publications or other scientific materials) and prepare a presentation which will be discussed in class with the lecturer and the peers.