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.
Modes of study
Option
1
Available for:
Degree Programme Students
Other Students
Open University Students
Doctoral Students
Exchange Students
Lectures, exercises, examParticipation in course work