Lecturer
Schedule
20 March 2023 – 24 March 2023
Monday–Friday, 09:00 – 16:00
10 lectures of 1.5 hours each, 10 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; Written final assignment
About the course
The course will cover the following topics related to non-neural-network-based machine learning:
- Neural networks for tabular data, images, and sequences
- Autoencoders
- Representation Learning
- Graph embeddings
- Regularisation, Optimisation, Approximate Inference
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 deep learning and what are the basic tools and techniques that it can offer us to model patterns in complex data by learning smart and efficient representaions.
Keywords
machine learning, statistics, complex networks, clustering, regression
Enrollment information
Enrollment is via ALMA
Tentative lecture plan
