Week11-1 -- Kernel methods II
Goal of Lecture 19
- Kernel ridge regression
- Reproducing kernel Hilbert Spaces (RKHS)
- kernel SVM and kernel PCA
Required reading
- kernel SVM: Chapter 9.3 in ISL (or Chapter 12.3 in ESL)
- RKHS: Chapter 5.8 in ESL
- Examples of kernels: Chapter 5.8.2 in ESL
- kernel PCA: Chapter 14.5.4 in ESL
Suggested reading
- Lecture notes on Kernel methods from CS229 at Stanford
- Chapter 12 - Reproducing kernel Hilbert spaces of High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin Wainwright (accessible via Duke library online)
- Review paper on kernel methods: Kernel Methods in Machine Learning
- Kernel Methods for Pattern Analysis by John Shawe-Taylor and Nello Cristianini (accessible via Duke library online)
