- 担当教員
- 杉山 将
- 使用教室
- 火3-4
- 単位数
- 講義：2 演習：0 実験：0
- 講義コード
- 76013
- シラバス更新日
- 2006年7月12日
- 講義資料更新日
- 2006年7月12日
- アクセス指標
-
- 学期
- 前期
講義概要
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.
講義の目的
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.
講義計画
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
教科書・参考書等
None. Handouts are distributed if necessary.
関連科目・履修の条件等
Probability and Statistics, Pattern Recognition
成績評価
Small reports related to machine learning and students' projects.
担当教員の一言
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.
その他
[Office Hours]
Anytime if available.
