Periods
(7-Jan-2014
– 7-Mar-2014)
(10-Mar-2014
– 16-May-2014)
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.
Please send email to mtt-studies@sis.uta.fi by wednesday 8.1. at the latest. After 8.1. contact the lecturer.
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.
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.
Please send email to mtt-studies@sis.uta.fi by wednesday 8.1. at the latest. After 8.1. contact the lecturer.
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.