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What is Regularization in Nonlinear Prediction about?

This lecture covers regularization techniques for fitting nonlinear functions to data using basis functions. It teaches how to: 1) Derive ridge regression and understand the tradeoff between fitting data and regularizing the model. 2) Use polynomial regression and cross-validation to deal with nonlinearity. 3) Choose hyperparameters like the regularization coefficient, kernel width, and polynomial order using cross-validation. 4) Understand how the number of data points and basis functions affect gen

Author

juanagallardo01

Language

EN