Algorithm $1$ is logistic regression $($you may use built$-$in methods for this$).$

Algorithm $2$ is gradient descent where you train a model of the form $\backslash$sigma $($w$$ x$)($with parameter w$)$ with respect to square loss, i$.$e$.$ the loss function is $21(\backslash$sigma $($w$$ x$)$ y$)2,$ averaged over the points in the training set $($code this up yourself, including calculating the gradient$).$

Algorithm $3$ is stochastic gradient descent again with respect to square loss, where during each iteration we use the gradient at one random point from the training set.