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Pattern Information Processing
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Tue 3-4Session W831

Credits  Lecture:2  Exercise:0  Experiment:0 / code:76013
Update syllabus´╝Ü2012/3/26
Update lecture notes : 2012/6/20
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Spring Semester

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, Advanced Data Analysis
Small reports related to machine learning and students' projects.
Comments 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.
Office Hours
Anytime if available.

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