Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.
Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.
Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Covers linear regression for estimating train speed using least squares and regularization.
Covers the concept of multi-linear regression and the least squares method for model fitting.
