Course: Applied Machine Learning credits: 5

Course code
SEVM19AML
Name
Applied Machine Learning
Study year
2022-2023
ECTS credits
5
Language
English
Coordinator
R.A.J. van Elburg
Modes of delivery
  • Lecture
  • Project-based learning
Assessments
  • Applied Machine Learning - Assignment

Learning outcomes

At the end of this module the student will reach the following learning outcomes:
  • 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.
 

Included in programme(s)

School(s)

  • Institute of Engineering
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