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Course unit, curriculum year 2021–2022
DATA.STAT.770

Dimensionality Reduction and Visualization, 5 cr

Tampere University
Teaching periods
Course code
DATA.STAT.770
Language of instruction
English
Academic years
2021–2022, 2022–2023, 2023–2024
Level of study
Advanced studies
Grading scale
General scale, 0-5
Persons responsible
Responsible teacher:
Jaakko Peltonen
Responsible teacher:
Tapio Nummi
Responsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 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
Prerequisites
Learning material
Kokonaisuudet, joihin opintojakso kuuluu
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.
Completion of all options is required.

Exam

No scheduled teaching

Participation in teaching

No scheduled teaching