Given
$$mathbf{theta}=
begin{Bmatrix}
theta_1 \
theta_2
end{Bmatrix}$$

$$J(theta)=(theta_1-5)^2+(theta_2-5)^2tag{1}$$

Derivate terms
$$frac{partial}{partialtheta_1}J(theta)=2(theta_1-5)$$
$$frac{partial}{partialtheta_2}J(theta)=2(theta_2-5)$$

We can get $min_{theta}J(theta)$ easily by observing $(1)$, i.e.
$$mathbf{theta}=
begin{Bmatrix}
5 \
5
end{Bmatrix}$$

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function [jVal, gradient] = costFunction(theta);
jVal = (theta(1)-5)^2 + (theta(2)-5)^2;
gradient = zeros(2,1);
gradient(1) = 2*(theta(1)-5);
gradient(2) = 2*(theta(2)-5);

costFunction returns two values: cost function value jVal and derivate terms value in a matrix type assign to gradient.

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options = optimset('GradObj','on','MaxIter','100');
initialTheta = zeros(2,1);
[optTheta, functionVal, exitFlag]...
 = fminunc(@costFunction, initialTheta, options);

fminunc is stands for function minimization unconstrained in Octave.

Remind that $thetainmathbb{R^d},dge2$

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theta = [theta0; theta1; theta2; ...; thetaN];
function [jVal, gradient] = costFunction(theta);
jVal = [code to compute J(theta)];
gradient(1) = [code to compute the first derivative to J(theta) with respect to theta0];
gradient(2) = [code to compute the first derivative to J(theta) w.r.t. theta1];
...
gradient(n+1) = [code to compute the first derivative to J(theta) w.r.t. thetaN];

Where

$$frac{partial}{partialtheta_0}J(theta)=frac{1}{m}sum_{i=1}^m[(h_{theta}(x^{(i)}))-y^{(i)}*x_0^{(i)}]$$
$$frac{partial}{partialtheta_1}J(theta)=frac{1}{m}sum_{i=1}^m[(h_{theta}(x^{(i)}))-y^{(i)}*x_1^{(i)}]$$