Maximum Likelihood Estimation: Linear Models

This lecture covers the concept of maximum likelihood estimation in the context of linear measurement models, where observations are assumed to be generated by unknown parameters and noise. The instructor explains how to estimate the parameters by maximizing the likelihood function, using examples with Gaussian and uniform noise. The lecture also delves into covariance estimation for Gaussian variables and the application of support vector machines (SVM) for classification problems, including hard and soft margin SVMs. The SVM optimization problems, hinge loss, and regularization terms are discussed, along with the primal and dual formulations of the SVM problem.