Material covered

• Kernels in local regression -- Nadaraya-Watson
• SVM dual, where to plug in the kernel
• Gram matrix and why it should be positive semi definite
• Eigen decomposition, embedding in feature space
• Polynomial kernels from first principles
• Rules for creating valid kernels by combining known kernels