Parametric Models: Mathematics of Data

This lecture covers the fundamentals of parametric models in data analysis, focusing on Gaussian linear regression, logistic regression, and Poisson regression. It delves into the estimation process, optimization problems, and regression estimators via probabilistic models. Examples such as Magnetic Resonance Imaging (MRI) and Breast Cancer Detection are used to illustrate these concepts. The lecture also explores the statistical models for photon-limited imaging systems and graphical model learning. Towards the end, it discusses the formal formulation and optimization of Google PageRank, providing insights into the role of computation and risk minimization strategies.