diff --git a/exercises/mlclass-ex1/gradientDescent.m b/exercises/mlclass-ex1/gradientDescent.m
index 2fde574..61ed7ab 100644
--- a/exercises/mlclass-ex1/gradientDescent.m
+++ b/exercises/mlclass-ex1/gradientDescent.m
@@ -6,7 +6,6 @@
 % Initialize some useful values
 m = length(y); % number of training examples
 J_history = zeros(num_iters, 1);
-num_vars=size(X,2);
 
 for iter = 1:num_iters
 
@@ -17,11 +16,7 @@
     % Hint: While debugging, it can be useful to print out the values
     %       of the cost function (computeCost) and gradient here.
     %
-  theta_tmp=zeros(num_vars,1);
-  for j=1:num_vars
-    theta_tmp(j)= theta(j) - (alpha/m) *  sum(((X*theta)-y).*X(:,j));
-  end
-  theta=theta_tmp;
+    theta = theta - (X' * (X * theta - y) * alpha / m);
     % ============================================================
 
     % Save the cost J in every iteration