Bedartha Goswami


21 March 2022 – 25 March 2022
Monday–Friday, 09:00 – 16:00
11 lectures of 2 hours each, 9 tutorials of 2 hours each


Seminar Room 4F03, Geo- und Umweltforschungszentrum (GUZ), Morgenstelle

Credits and Workload

Workload: 60 h
Contact hours: 40 h
Self study: 20 h
Duration: 1 week Medium of instruction: English


Presentation of recap in class; Written project report

About the course

The course will cover the following topics relate to dimensionality reduction and feature extraction

  • Component analyses (PCA, ICA, NMF, etc)
  • Variational autoencoders
  • Climate networks and community detection
  • Disentanglement
  • Other methods: (self-organizing maps, t-SNE, …)

Teaching methods

The lectures are designed to be interactive and dialogic. The final evaluation will be on the basis of of an oral presentation (recap) and a written report.

Learning target

At the end of the course, students will posses a basic understanding of what is machine learning and what are the basic tools and techniques that it can offer us to understand complex systems in low dimensional representaions.


machine learning, statistics, complex networks, clustering, regression

Enrollment information

Enrollment is via ALMA

Tentative lecture plan


You can find the codes that are being discussed in the tutorial at the following Github repository: