Explores linear regression with and without covariates, covering models captured by independent distributions and tools like subspaces and orthogonal projections.
Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.
Covers the system model, linear receivers, performance evaluation, and graphical interpretations of MIMO receivers, including maximum likelihood detection and successive interference cancellation.
