Lecturer
Schedule
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
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; 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.
Keywords
machine learning, statistics, complex networks, clustering, regression
Enrollment information
Enrollment is via ALMA
Tentative lecture plan
