Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.
Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.
Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.
