Recursive Least-Squares: Weighted Formulation

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Covers the theoretical properties of the linear regression estimator, including the hat matrix and the Gauss-Markov theorem.

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Explores quantile regression for electricity price prediction using time series data, regularization, and the kernel trick.

Explains least squares approximation for finding best fit lines or curves to data points.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.