Supervised and Unsupervised Machine Learning Algorithms on Morphological Classifications of Galaxies

- Bachelorarbeit -

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Description:

The purpose of this thesis is to use supervised and unsupervised machine learning algorithms to classify observed galaxies into morphological classes. For this application, data from the SDSS dataset is used. The aim is to explore the advantages of supervised and unsupervised algorithms in the task of categorization of the galaxies, which is very important for astrophysics.

Unlike supervised ones, unsupervised algorithms were not employed much for galaxy categorization in the past. During the process of this thesis, unsupervised learning methods will be implemented for galaxy classification, while existing implementations of the supervised methods will be replicated. As the final product, an extensive comparison between all the implemented methods will be produced to guide future research in the field.

Anforderungen/Kenntnisse:

Machine Learning

Bearbeitung:

C. Groß

Betreuung:

Prof. Dr. rer. nat. habil. Sven Groppe
Institut für Informationssysteme
Ratzeburger Allee 160 ( Gebäude 64 - 2. OG)
23562 Lübeck
Telefon: 0451 / 3101 5706