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Advanced Data Analysis
( Sugiyama Masashi  )


Tue 3-4Session

Credits  Lecture:2  Practice:0  Experiment:0 / code:76033
Update : 2005/7/12
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Spring Semester

Outline of lecture
The objective of this course is to introduce basic ideas and practical methods of discovering useful structure hidden in the data.
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
None. Handouts are distributed if necessary.
Related and/or prerequisite courses
Probability and Statistics, Pattern Recognition
Evaluation
Reports related to 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.
Supplement
[Office Hours]
Anytime if available.

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