Q$3)$

In this problem, we look at maximum likelihood parameter estimation using the naive Bayes assumption. Here, the input features $x_j,j=1,$dots,$n$ to our model are discrete, binary$-$valued variables, so $x_{j}in\{0,1\}.$ We call $x={\left[\begin{array}{cccc}{x}_1& x_2& \text{c}\text{d}\text{o}\text{t}\text{s}& x_n\end{array}\right]}^T$ to be the input vector. For each training example, our output targets are a single binary$-$value yin$\{0,1\}.$ Our model is then parameterized by $|)$

Please solve the derivatives in part b