- Place
- Tue3-4
- Credits
- Lecture2 Exercise0 Experiment0
- Code
- 76033
- Syllabus updated
- 2007/7/4
- Lecture notes updated
- 2007/7/4
- Access Index
-
- Semester
- 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.
