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.