diff --git a/ex5/linearRegCostFunction.m b/ex5/linearRegCostFunction.m
index 6addf6b..1d70418 100644
--- a/ex5/linearRegCostFunction.m
+++ b/ex5/linearRegCostFunction.m
@@ -1,34 +1,26 @@
 function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
-%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear 
+%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear
 %regression with multiple variables
-%   [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the 
-%   cost of using theta as the parameter for linear regression to fit the 
+%   [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the
+%   cost of using theta as the parameter for linear regression to fit the
 %   data points in X and y. Returns the cost in J and the gradient in grad
 
 % Initialize some useful values
 m = length(y); % number of training examples
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 J = 0;
 grad = zeros(size(theta));
 
 % ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost and gradient of regularized linear 
+% Instructions: Compute the cost and gradient of regularized linear
 %               regression for a particular choice of theta.
 %
 %               You should set J to the cost and grad to the gradient.
 %
 
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+J =   1/(2*m) * sum(((X*theta)-y).^2) ...
+    + lambda/(2*m) * sum(theta(2:end).^2);
 
 % =========================================================================