apply single layer linear Perceptron learning algorithm to below dataset. Perform a

maximum of four iterations one for each data point. Show the equation of the

separating line at the end of four iterations. For full credit show your step by step

work. Assume initial weights for $[$X$1,$ X$2,$ bias$][$w$1,$ w$2,$ w$3]$ as $[0\mathrm{.}75,0\mathrm{.}5,-0\mathrm{.}6]$ and

learning rate L $=0\mathrm{.}2.\backslash$theta $=0.$ Bias is always $1.$

Use activation function, f$($net$),$ where Wm is weight vector, Xm the input vector:

f$($net$)$ returns $1$ if net $>=\backslash$theta where net $=\backslash$Sigma wixi

else $-1($if net $<\backslash$theta $)$