Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

Kernel Ridge Regression: Equivalence, Representer Theorem, and Kernel Trick

Explores Kernel Ridge Regression, the Representer Theorem, and the Kernel Trick in machine learning.

Kernel Ridge Regression and the Kernel Trick

Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.

Learning the Kernel: Convex Optimization

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.