Lecturer
Schedule
21 March 2022 – 25 March 2022
Monday–Friday, 09:00 – 16:00
11 lectures of 2 hours each, 9 tutorials of 2 hours each
Location
Seminar Room 4F03, Geo- und Umweltforschungszentrum (GUZ), Morgenstelle
Credits and Workload
ECTS: 2
Workload: 60 h
Contact hours: 40 h
Self study: 20 h
Duration: 1 week
Medium of instruction: English
Evaluation
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.
Keywords
machine learning, statistics, complex networks, clustering, regression
Enrollment information
Enrollment is via ALMA
Tentative lecture plan
Link to tutorial code
You can find the codes that are being discussed in the tutorial at the following Github repository: