Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.
