Least Squares Estimate and Projectivity

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Method of Least Squares: Normal Equations

Explains the method of least squares and normal equations for finding optimal solutions.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Linear Regression: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Regression: Linear Models

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

Distributed Computation: Consensus Algorithms in Networked Control Systems

Explores consensus algorithms in networked control systems, covering topics like Metropolis-Hasting models and distributed computation of Least-Squares regression.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Linear Regression: Basics and Applications

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

Linear Regression: Regularization

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