Summary: The text discusses kernelizing the perceptron algorithm and constructing kernels, along with kernelized linear regression. It explains modifying the perceptron algorithm to keep track of misclassifications and how to compute predictions using kernelized linear regression. The importance of kernel matrices and the advantages of using a non-zero regularization parameter in kernelized ridge regression are also highlighted.
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