Parametric Models: Estimation and Optimization

This lecture covers the mathematics behind parametric models, focusing on statistical estimation and optimization techniques. It explains the concept of parametric estimation models, maximum-likelihood estimators, and regression estimators via probabilistic models. The instructor discusses examples such as Gaussian linear regression, logistic regression, and Poisson regression models. The lecture also delves into the application of parametric models in real-world scenarios like Magnetic Resonance Imaging (MRI) and Breast Cancer Detection. Furthermore, it explores score-based classifiers and the use of score functions to predict outcomes. The session concludes with M-estimators and their application in graphical model learning for statistical inference.