Linear Models: Continued

This lecture delves into linear models, focusing on linear regression, matrix form representation, and multi-output prediction. It covers exercises on solution interpretation, binary classification, multi-class problems, and logistic regression training. The instructor explains the concept of adding non-linearity, the logistic sigmoid function, and probabilistic interpretation. The lecture concludes with discussions on iterative, gradient-based optimization, minimizing convex and non-convex functions, and gradient descent for multi-variate functions.