Linear Classification: Variants and Stochastic Gradient Descent

This lecture covers the variants of gradient descent used in practice, focusing on stochastic gradient descent (SGD) and its properties. It explains how SGD works by choosing a minibatch of samples at every iteration, approximating the full derivative. The lecture also delves into linear classification, discussing the concept of a linear separator and the importance of having differential properties almost everywhere.