Mean absolute percentage error

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Explores linear prediction, prediction coefficients, mean squared error minimization, and the Levinson-Durbin algorithm in signal processing.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Explores sampling strategies for signals, emphasizing the importance of controlling errors in the sampling process.

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers residuals, diagnostics, overfitting, and forecasting methods in time series analysis.

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

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Covers Gaussian Acyclic Models focusing on linearity and identifiability.