Parametric Models: Regression Estimators and Optimization

This lecture covers the fundamentals of parametric models, including Gaussian linear regression, logistic regression, and Poisson regression. It delves into the concept of parametric estimation models, statistical estimation, and maximum-likelihood estimators. The lecture also explores regression estimators via probabilistic models, with examples such as Magnetic Resonance Imaging (MRI) and Breast Cancer Detection. Additionally, it discusses the ML estimator for MRI, the statistical model for photon-limited imaging systems, and M-estimators. The lecture concludes with a detailed explanation of graphical model selection, Google PageRank modeling, and the optimization formulation of Google PageRank.