Linear Models: Least Squares and QR Factorization

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Explores linear regression through least squares and normal equations, emphasizing the importance of minimizing errors for accurate predictions.

Explores Singular Value Decomposition, matrix solutions, and least squares regression in data analysis.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Covers the factorization of a matrix and the least squares method.

Explores linear regression analysis of ozone data using statistical models.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

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

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