Linear Models: Least Squares and QR Factorization

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

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

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

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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