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Lecture: 01-29-03-ML-V Machine Learning for Swarm Exploration - Details

Lecture: 01-29-03-ML-V Machine Learning for Swarm Exploration - Details

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General information

Course name Lecture: 01-29-03-ML-V Machine Learning for Swarm Exploration
Subtitle
Course number 01-29-03-ML-V
Semester WiSe 2022/2023
Current number of participants 32
Home institute Elektrotechnik
Courses type Lecture in category Teaching
First date Monday, 06.02.2023 09:00 - 17:00, Room: NW1 N3130
Type/Form
Englischsprachige Veranstaltung Ja
ECTS points 3

Rooms and times

NW1 N3130
Monday, 06.02.2023 - Friday, 10.02.2023 09:00 - 17:00

Comment/Description

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

Registration mode

After enrolment, participants will manually be selected.

Potential participants are given additional information before enroling to the course.