Bedartha Goswami


6 March 2023 – 10 March 2023
Monday–Friday, 09:00 – 16:00
11 lectures of 1.5 hours each, 9 tutorials of 1.5 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; Oral presentation or project

About the course

The course will cover the following topics related to non-neural-network-based machine learning:

  • Basics of machine learning concepts
  • Basics of probability
  • Basics of linear algebra
  • Linear regression
  • Principal component analyses
  • Clustering
  • Complex networks
  • Gaussian Processes
  • Causal Inference
  • Kernel methods
  • Markox models

Teaching methods

The lectures are designed to be interactive and dialogic. Lectures are going to be conducted with a black board (no slide presentation). Tutorials will be conducted with a laptop.

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