Lecture
This lecture covers the basics of statistical estimation, focusing on the Gaussian linear model. Topics include parametric estimation models, maximum-likelihood estimators, least-squares estimators, loss functions, and practical issues such as data size impact and computation role. The instructor emphasizes the performance of estimators, the ML estimator's limitations, and the comparison with the James-Stein estimator.