Generalized Linear Regression: Classification

This lecture covers Generalized Linear Regression, Multiple Linear Classification, Evaluating Binary Classification, and Poisson Regression. It explains the likelihood maximization, supervised learning, confusion matrices, ROC curves, AUC, and noise in data. The instructor demonstrates the application of logistic regression, classification, and regression models on various datasets.