# Pattern Information Processing

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Tue 3-4Session W831

Credits Lecture:2 Exercise:0 Experiment:0 / code:76013

Update syllabus：2012/3/26

Update lecture notes : 2012/6/20

Access Index :

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
- Evaluation
- 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.