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Advanced Data Analysis
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Tue 3-4Session W831

Credits  Lecture:2  Exercise:0  Experiment:0 / code:76033
Update syllabus´╝Ü2011/4/6
Update lecture notes : 2011/6/22
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Spring Semester

Purpose of lecture
The objective of this course is to introduce basic ideas and practical
methods of discovering useful structure hidden in the data.
Plan of lecture
1. Introduction
2. Pseudo Biorthogonal Basis
3. Principal Component Analysis
4. Kernel Principal Component Analysis
5. Non-Gaussian Component Analysis
6. Spectral Methods of Dimensionality Reduction
7. K-means Clustering
8. Spectral Clustering
9. Outlier Detection
10. Kernel Outlier Detection
11. Independent Component Analysis
12. Blind Source Separation
13. Concluding Remarks and Future Prospects
Textbook and reference
Handouts are provided if necessary.
Pointers to relevant material will be provided.
Related and/or prerequisite courses
Pattern Information Processing
Pattern Recognition (in Japanese)
Probability Theory and Statistics (in Japanese)
Reports related to intelligent data analysis and students' projects.
Comments from lecturer
In order to really learn the methods, it is important to actually use
them. Analyzing your own data using the learned methods is expected.

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