Model Selection: Non-Nested Model Selection

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Page 3 of 4

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

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

Explores linear regression analysis of ozone data using statistical models.

Explores over-learning, generalization, and under-learning in machine learning models.

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Covers the probabilistic model for linear regression and the importance of regularization techniques.