講義概要
The objective of this course is to introduce basic ideas and practical methods of discovering useful structure hidden in the data.
講義の目的
The objective of this course is to introduce basic ideas and practical methods of discovering useful structure hidden in the data.
講義計画
1. Introduction
2. Principal Component Analysis
3. Locality Preserving Projection
4. Fisher Discriminant Analysis
5. Kernel Principal Component Analysis
6. Kernels
7. K-Means Clustering
8. Spectral Clustering
9. Projection Pursuit 1
10. Projection Pursuit 2
11. Blind Source Separation
12. Non-Gaussian Component Analysis
13. Concluding Remarks and Future Prospects
教科書・参考書等
None. Handouts are distributed if necessary.
関連科目・履修の条件等
Probability and Statistics, Pattern Recognition
成績評価
Reports related to data analysis and students' projects.
担当教員の一言
In order to really learn the methods, it is important to actually use them. Analyzing your own data using the learned methods is expected.
その他
[Office Hours]
Anytime if available.
