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Course unit, curriculum year 2022–2023
DATA.STAT.770

Dimensionality Reduction and Visualization, 5 cr

Tampere University
Teaching periods
Active in period 3 (1.1.2023–5.3.2023)
Active in period 4 (6.3.2023–31.5.2023)
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
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.
Completion of all options is required.

Exam

09.01.2023 31.05.2023
Active in period 3 (1.1.2023–5.3.2023)
Active in period 4 (6.3.2023–31.5.2023)

Participation in teaching

10.01.2023 31.05.2023
Active in period 3 (1.1.2023–5.3.2023)
Active in period 4 (6.3.2023–31.5.2023)