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Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.
Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Covers regularization in least-squares problems, promoting optimal solutions while addressing challenges like non-uniqueness, ill-conditioning, and over-fitting.
Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
