The objective of this course is to introduce basic ideas and practical
methods of discovering useful structure hidden in the data.
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
Handouts are provided if necessary.
Pointers to relevant material will be provided.
Pattern Information Processing
Pattern Recognition (in Japanese)
Probability Theory and Statistics (in Japanese)
Reports related to intelligent data analysis and students' projects.
In order to really learn the methods, it is important to actually use
them. Analyzing your own data using the learned methods is expected.