07 - 11 October 2024
Schedule for students of the M.Sc. program Environmental Physics: https://www.pep.uni-bremen.de/downloads/e_woche_24_25_pep_hp.pdf
Schedule for students of the M.Sc. program Space Sciences and Technologies: https://www.uni-bremen.de/fileadmin/user_upload/fachbereiche/fb1/fb1/FB1/Dokumente/Studium/Sonstige/E_Woche_24_25_Space_HP.pdf

lecture: HS 2010 (großer Hörsaal)
practical fire drill: Emmy-Noether-Str. behind the SFG Building

The practical fire drill takes place outdoors. Please wear weatherproof clothing and suitable footwear.

Please check StudIP for the exact course days.

This course has 2 semester weekly hours (1,5x lecture + 0,5 example classes). The course will start on 27 November 2024.

Block-Course (around end of semester)

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

In this seminar, we will study foundational, historical, and philosophical questions of cosmology, of space and time, and of space travel. The syllabus comprises topics and concepts such as key elements of the cosmological standard model, of cosmology as a science and how contemporary research benefits from an awareness of its historical development, and of further epistemological and methodological questions regarding, for instance, the heavy reliance on strong idealisations and extrapolations.


Prerequisites:
Formally, no prerequisite knowledge in either cosmology or philosophy is needed, but a willingness to engage in both is essential.


Learning outcome/learning goals:
• Basic knowledge of cosmology and of the physics of space and time
• Knowledge of basic relevant notions from the philosophy of the natural sciences
• Basic insights into the aims of scientific inquiry and the generation of scientific knowledge

In this seminar, we will study foundational, historical, and philosophical questions of cosmology, of space and time, and of space travel. The syllabus comprises topics and concepts such as key elements of the cosmological standard model, of cosmology as a science and how contemporary research benefits from an awareness of its historical development, and of further epistemological and methodological questions regarding, for instance, the heavy reliance on strong idealisations and extrapolations.


Prerequisites:
Formally, no prerequisite knowledge in either cosmology or philosophy is needed, but a willingness to engage in both is essential.


Learning outcome/learning goals:
• Basic knowledge of cosmology and of the physics of space and time
• Knowledge of basic relevant notions from the philosophy of the natural sciences
• Basic insights into the aims of scientific inquiry and the generation of scientific knowledge