Linear Regression using fminunc Implementation

I'm trying to implement linear regression with only one feature using fminunc in Octave.

Here is my code.

x = load('/home/battousai/Downloads/ex2Data/ex2x.dat');
y = load('/home/battousai/Downloads/ex2Data/ex2y.dat');

m = length(y);
x = [ones(m , 1) , x];
theta = [0 , 0]';

X0 = [x , y , theta];

options = optimset('GradObj' , 'on' , 'MaxIter' , 1500);
[x , val] = fminunc(@computeCost , X0 , options) 

And here is the cost function which returns the gradient as well as the value of the cost function.

function [J , gradient] = computeCost(x , y , theta)
  m = length(y);
  J = (0.5 / m) .* (x * theta - y )' * (x * theta - y );
  gradient = (1/m) .* x' * (x * theta - y);
end

The length of the data set is 50, i.e., the dimensions are 50 x 1. I'm not getting the part that how should I pass X0 to the fminunc.

Updated Driver Code:

x = load('/home/battousai/Downloads/ex2Data/ex2x.dat');
y = load('/home/battousai/Downloads/ex2Data/ex2y.dat');

m = length(y);
x = [ones(m , 1) x];
theta_initial = [0 , 0];
options = optimset('Display','iter','GradObj','on' , 'MaxIter' , 100);
[X , Cost] = fminunc(@(t)(computeCost(x , y , theta)), theta_initial , options) 

Updated Code for Cost function:

function [J , gradient] = computeCost(x , y , theta)
  m = length(y);
  J = (1/(2*m)) * ((x * theta) - y )' * ((x * theta) - y) ;
  gradient = (1 / m) .* x' * ((x * theta) - y);  
end

Now I'm getting values of theta to be [0,0] but when I used normal equation, values of theta turned out to be [0.750163 , 0.063881].