We have mainly focused on squared loss, but there are other interesting losses in machine learning. Consider the following loss function which we denote by $\backslash$phi $($z$)=$ max$(0,$z$).$ Let S be a training set $($x$1,$y$1),...,($xm$,$ym$)$ where each xi in Rn and yi in $\{1,1\}.$ Con$-$ sider running stochastic gradient descent $($SGD$)$ to find a weight vector w that minimizes

m$1$ Pmi$=1\backslash$phi $($yi $$ wT xi$).$ Explain the explicit relationship between this algorithm and the Per$-$ ceptron algorithm. Recall that for SGD$,$ the update rule when the ith example is picked at random is

wnew $=$ wold $\backslash$eta $\backslash$phi yiwT xi $.$