Course: Applied Machine Learning credits: 5
- Course code
- SEVM19AML
- Name
- Applied Machine Learning
- Study year
- 2026-2027
- ECTS credits
- 5
- Language
- English
- Coordinator
- S.S. Ahmed
- Modes of delivery
-
- Lecture
- Project-based learning
- Assessments
-
- Applied Machine Learning - Assignment
Learning outcomes
At the end of this module the student:
- the student can analyse a data analysis task and compare features to evaluate their fitness for the machine learning task at hand.
- the student can evaluate whether a data analysis task requires dimensionality reduction, blind source separation, cluster analysis or classification.
- the student can interpret high dimensional data using dimensionality reduction techniques such as principal component analysis.
- the student can interpret multi-sensor data:
- using blind source separation techniques like independent component analysis.
- using cluster analysis techniques like hierarchical agglomerative clustering and k-means.
- using classification algorithms like support vector machines.
- the student can apply anomaly detection on a stream of sensor data.
Content
The course covers the following topics:
machine learning overview, dimensionality reduction, blind source separation, cluster analysis or classification, coverage plot, confusion matrix, cost functions.
machine learning overview, dimensionality reduction, blind source separation, cluster analysis or classification, coverage plot, confusion matrix, cost functions.
Included in programme(s)
School(s)
- Engineering, Life Sciences & ICT