Linear Least Squares: Minimizing Error Coefficients

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Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

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Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.

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

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.