Generative Models: Logistic Regression & Gaussian Distribution

This lecture covers the concept of generative models, focusing on logistic regression and Gaussian distribution. It explains how generative models approximate posterior probability distributions and how logistic regression parametrizes the posterior probability. The instructor discusses the process of training generative models on data sets, estimating parameters, and computing posterior probabilities. The lecture also delves into the importance of choosing appropriate parameters, such as Sigma and C, in generative models to optimize model performance.