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Course unit, curriculum year 2019–2020
MTTTS17
Dimensionality Reduction and Visualization, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 3 (1.1.2020–1.3.2020)
Active in period 4 (2.3.2020–31.5.2020)
Course code
MTTTS17Language of instruction
EnglishAcademic year
2019–2020Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Jaakko PeltonenResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
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.
Learning outcomes
Learning material
Studies that include this course
Completion option 1
To pass the course, you must pass the exam and complete a sufficient number of exercises from the exercise packs. Exercise packs will be released during the course.
Participation in teaching
07.01.2020 – 15.05.2020
Active in period 3 (1.1.2020–1.3.2020)
Active in period 4 (2.3.2020–31.5.2020)