GOALS:
Students will become familiar with the fundamental principles and methods of machine learning. They will be able to explain the key concepts, problems, and algorithms of statistical machine learning.
By the end of the course, students should be able to identify suitable machine learning methods for a given technical problem, compare different approaches, and justify their choice of method.
Content:
This module introduces the fundamental principles and basic algorithms of statistical machine learning.
Topics include, but are not limited to:
- Supervised Learning
- Bayesian Decision Theory
- Parametric Methods
- Multivariate Methods
- Dimensionality Reduction
- Clustering
- Non-Parametric Methods
- Decision Trees
- Neural Networks
- Reinforcement Learning
MISCELLANEOUS:
Further information regarding course organization, exercises, and assessment will be provided during the course and on Moodle.
EXAM:
Written (90 minutes), Registration: FlexNow
- Kursleiter/in: Alireza Habibi
- Kursleiter/in: Setareh Maghsudi
- Kursleiter/in: Klara Leoni Wallbaum
Semester: WT 2026/27