Kernel Regression: Basics and Applications

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Description

This lecture covers the fundamentals of kernel regression, including the concept of kernels as alternative scalar products, the use of kernel regression for prediction, and the Representer Theorem. It also explores the curse of dimensionality in neural networks and the application of random features.

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Eiusmod aliquip officia velit irure minim sit. Dolore veniam velit proident nostrud veniam Lorem cillum pariatur laboris mollit. Pariatur exercitation aliquip cillum esse duis velit.

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Kernel Methods: Machine Learning

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