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.

Parts of the exercise will be held as a seminar (dates and time slots upon agreement with the students).

As a replacement for the module Atmospheric Aerosols in the winter semester 2021/2022, students of Space-ST, specialization Physics for Space Observations, choose either: 01-01-03-CliS2-V Climate System II (3CP) or 01-01-03-GCC-V Global Carbon Cycle (3CP) or 01-01-03-MRS-V Microwave Remote Sensing (3CP) from the section "Options"

This course has 2 semester weekly hours (1,5x lecture + 0,5 example classes). The course will start on 09 December 2021.

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

Course content:

In this seminar, we shall study foundational, historical and philosophical questions of cosmology. The syllabus comprises topics and concepts such as (A) key elements of the cosmological standard model and anomalies within it; (B) cosmology as a science and how contemporary research benefits from an awareness of its historical development; (C) further epistemological and methodological questions regarding, for instance, the heavy reliance on strong idealisations and extrapolations in cosmology.
Formally, no prerequisite knowledge in either cosmology or philosophy is needed (albeit desirable). Diligent preparation of the weekly required reading, and continuous active participation are obligatory for successfully completing this course (See below for the details regarding the course requirements/Leistungsnachweis).

Learning outcome/learning goals:

• Basic knowledge of cosmology.
• Knowledge of basic relevant notions from the philosophy of the natural sciences (natural law, space, time, infinity, …)
• Basic insights into the aims of scientific inquiry and the generation of scientific knowledge (by means of examples from the history of cosmology)

Preparatory literature:

- P. Coles. Cosmology – a Very Short Introduction. Oxford University Press, 2013
- C. Smeenk & G. Ellis. Philosophy of Cosmology. Stanford Encyclopedia of Philosophy, 2017, https://plato.stanford.edu/entries/cosmology/