Update syllabus：2007/7/4

Update lecture notes : 2007/7/4

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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. 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 - 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.