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Arkistoitu opetusohjelma 2014–2015
Selaat vanhentunutta opetusohjelmaa. Voimassa olevan opetusohjelman löydät täältä.
MTTS1 Dimensionality reduction and visualization 5 ECTS
Period I Period II Period II Period IV
Language of instruction
Type or level of studies
Advanced studies
Course unit descriptions in the curriculum
Matematiikan ja tilastotieteen tutkinto-ohjelma
School of Information Sciences

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.

General description

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.

Enrolment for University Studies

Enroll by sending e-mail to the lecturer (jaakko.peltonen@uta.fi) by 6.1.2015 at the latest. After this, participate to the first lecture.


Jaakko Peltonen, Teacher responsible

Homepage URL


Mon 12-Jan-2015 - 11-May-2015 weekly at 14-16, Pinni B0020, no lectures on mondays 9-Mar or 6-Apr


Numeric 1-5.

Further information

Modes of study

- Lectures
- Exercises (independent work)
- Exam

Recommended preceding studies

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


Course can be an optional course in
- Advanced Studies in Statistics
- Advanced Studies in Computational Methods and Programming
- M.Sc. programme in Algorithmics

Further information on including this course in advanced studies, contact your study advisor or professor.