Physical presence required! Registration from 7:30 a.m. Note the COVID-19 precaution measures: Keep your distance, wear a mask.
Participation in a fire safety exercise is mandatory for all students whose study programme contains laboratory work. Admittance to lab work only after participation in this fire drill.
08:00 am - 09:00 am lecture
09:00 am - 11:00 am practical fire protection exercises outdoors, so please wear weatherproof clothing and sturdy shoes!

Course will start online.

Course will start online.

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 2020/2021, 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 online on 07 January 2021.

Course will start online.

Course will start online.

Course will start online.

Please use this Stud.IP only if you have previously registered for the exam.

Course will start online.

Elective course for M.Sc. CIT, Electrical Engineering & Information Technology and Space-ST. Block course at the end of the lecture period in February 2021.
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:

This course covers philosophical questions about cosmology and about the exploration of terra incognita related to space. First, we cover the meaning of exploration for mankind in general (exploration of new territories as well as of laws of the physical world and laws in general). Second, we specialize to questions related to space: What is the idea behind a finite or infinite world? What does the exploration of space mean for the “position” of mankind within the Universe, for the world view of human beings? What would it mean for mankind if the search for extraterrestrial life will be successful? In what sense can cosmology missions “uncover” the dynamics of the universe from the Big Bang to the far future? What concept of time is involved here and what counts as evidence and why?

Learning outcome/learning goals:

• Knowledge of basic 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)
• Ideas involved in human self-understanding related to “other worlds” or extraterrestrial life
• Basic knowledge of cosmology.

Literature:
[Will be announced at the beginning of the course.]