Training, validation, and test data sets

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Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduces techniques for obtaining unbiased risk estimates of learned predictors and their application for hyperparameter tuning.

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

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Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.