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Pattern Information Processing
(
Sugiyama Masashi
)
Tue 3-4Session W831
Credits Lecture:2 Practice:0 Experiment:0 / code:76013
Update : 2008/7/1
Access Index :
Spring Semester
- Outline of lecture
- Inferring an underlying input-output dependency from input and output examples is called supervised learning. This course focuses on a statistical approach to supervised learning and introduces its basic concepts as well as state-of-the-art techniques.
- Purpose of lecture
- Inferring an underlying input-output dependency from input and output examples is called supervised learning. This course focuses on a statistical approach to supervised learning and introduces its basic concepts as well as state-of-the-art techniques.
- Plan of lecture
- 1. Introduction
2. Statistical Formulation of Supervised Learning
3. Linear, Kernel, and Non-Linear Models
4. Least-Squares Learning
5. Weighted Least-Squares Learning
6. Regularization Learning
7. Sparse Learning
8. Robust Learning
9. Error Back-Propagation Algorithm
10. Cross-Validation
11. Input-Dependent Estimation of Generalization Error
12. Active Learning
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
- Small reports related to machine learning and students' projects.
- Comment from lecturer
- Statistical machine learning is an interdisciplinary subject with a wide range of applicability. Not only learning basic foundations of machine learning, but also applying the learned knowledge to their own research topics is expected.
- Supplement
- [Office Hours]
Anytime if available.
