Lectures related to Method of Least Squares: Normal Equations

Covers linear regression, regularization, and probabilistic models in generating labels.

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

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Linear Regression: Estimation and Prediction

Covers the basics of linear regression, focusing on estimation and prediction.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Linear Regression: Basics and Applications

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

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Modeling Response Surfaces in Matlab

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.