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Advanced Data Analysis
(
Sugiyama Masashi
)
Tue 3-4Session W831
Credits Lecture:2 Practice:0 Experiment:0 / code:76033
Update : 2011/6/22
Access Index :
Spring Semester
- 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
- Handouts are provided if necessary.
Pointers to relevant material will be provided. - Related and/or prerequisite courses
- Pattern Information Processing
Pattern Recognition (in Japanese)
Probability Theory and Statistics (in Japanese) - Evaluation
- Reports related to intelligent data analysis and students' projects.
- Comment 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.
