Kernel-Based Learning

Kernel-Based Learning

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by University of California

The main goal of this course is to introduce the students to one of the most influential developments in modern machine learning, namely kernel methods. The course will be focused on familiarizing the student with a number of practical kernel-based algorithms
(such as “support vector machines”, “kernel Fisher Discrimination”,
“kernel principal components analysis” and “Gaussian processes”) and a number of techniques to construct kernels (such as ANOVA kernels, string kernels, graph kernels, diffusion kernels, set kernels). The necessary learning-theoretic preliminaries will be treated as well but it will not be the focus of this course. Applications to real-world problems will serve as examples.


Source: The University of California

MODULE 1: lecture notes

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