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.

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.

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. 

Other

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.