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.
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
11. Input-Dependent Estimation of Generalization Error
12. Active Learning
13. Concluding Remarks and Future Prospects
None. Handouts are distributed if necessary.
Probability and Statistics, Pattern Recognition, Advanced Data Analysis
Small reports related to machine learning and students' projects.
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.
Anytime if available.