x !
Arkistoitu opetussuunnitelma 2017–2019
Selaamasi opetussuunnitelma ei ole enää voimassa. Tarkista tiedot voimassa olevasta opetussuunnitelmasta.
MTTTS17 Dimensionality Reduction and Visualization 5 op
Organised by
Degree Programme in Mathematics and Statistics
Corresponding course units in the curriculum
Informaatiotieteiden yksikkö
Curricula 2015 – 2017

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.


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, exam  Participation in course work 
In English


Numeric 1-5.

Belongs to following study modules

Luonnontieteiden tiedekunta
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Luonnontieteiden tiedekunta