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/

HS 1010 (kleiner Hörsaal)
für die Feuerlöschübung: Emmy-Noether-Str. hinter dem SFG Gebäude

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

Block-course towards the end of the lecture period.