Theta is a Dx1 matrix and X is a NxD matrix. The sum is essentially standard matrix multiplication, except just one row of X

Recall D-dimensional regression problem when your model performs label prediction for the i-th examplex, in the training data set using linear function: f(xi; 09,01,02, ..., 0)) = Ed=10dxi(d) +00. In this case x, is D-dimensional. Let y, denote the true label of the i-th example and let N be the total number of training examples. Parameters of the model (0.,01,02, ..., 0p) are obtained by minimizing the empirical risk provided below: R(O) = 2.vlly x0|13 + 07H0+ 0+0 + ao, where a is a vector and H is a matrix that satisfies the condition: H = H". Both a and H are given. Write what is y, X, and in the formula above. Compute the optimal setting of parameters by setting the gradient of the risk to 0. Explain all steps in your derivations. Recall D-dimensional regression problem when your model performs label prediction for the i-th examplex, in the training data set using linear function: f(xi; 09,01,02, ..., 0)) = Ed=10dxi(d) +00. In this case x, is D-dimensional. Let y, denote the true label of the i-th example and let N be the total number of training examples. Parameters of the model (0.,01,02, ..., 0p) are obtained by minimizing the empirical risk provided below: R(O) = 2.vlly x0|13 + 07H0+ 0+0 + ao, where a is a vector and H is a matrix that satisfies the condition: H = H". Both a and H are given. Write what is y, X, and in the formula above. Compute the optimal setting of parameters by setting the gradient of the risk to 0. Explain all steps in your derivations