Learning outcomes

After the course, the student will be aware of main approaches and issues in dimensionality reduction and visualization, will be aware of a variety of methods applicable to the tasks, and will be able to apply some of the basic techniques.

Contents

Properties of high-dim data; Feature Selection; Linear feature extraction methods such as principal component analysis and linear discriminant analysis; Graphical excellence; Human perception; Nonlinear dimensionality reduction methods such as the self-organizing map and Laplacian embedding; Neighbor embedding methods such as stochastic neighbor embedding and the neighbor retrieval visualizer; Graph visualization; Graph layout methods such as LinLog.

Further information on prerequisites and recommendations

Basic mathematics and probability courses; basic competence in a scientific programming language such as matlab or R.

Evaluation

Belongs to following study modules

2017–2018

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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