--- a/computeCostMulti.m
+++ b/computeCostMulti.m
@@ -1,22 +1,8 @@
 function J = computeCostMulti(X, y, theta)
-%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
-%   J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
-%   parameter for linear regression to fit the data points in X and y
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
+##COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
+##   J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
+##   parameter for linear regression to fit the data points in X and y
 
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta
-%               You should set J to the cost.
-
-
+  J = sum ((X*theta - y).^2)/(2*rows (y));
 
-
-
-% =========================================================================
-
-end
+endfunction

--- a/gradientDescentMulti.m
+++ b/gradientDescentMulti.m
@@ -1,37 +1,18 @@
-function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
-%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
-%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
-%   taking num_iters gradient steps with learning rate alpha
-
-% Initialize some useful values
-m = length(y); % number of training examples
-J_history = zeros(num_iters, 1);
-
-for iter = 1:num_iters
-
-    % ====================== YOUR CODE HERE ======================
-    % Instructions: Perform a single gradient step on the parameter vector
-    %               theta. 
-    %
-    % Hint: While debugging, it can be useful to print out the values
-    %       of the cost function (computeCostMulti) and gradient here.
-    %
+function [theta, J_history] = gradientDescentMulti(X, y, 
+                                                   theta, alpha, num_iters)
+  ##GRADIENTDESCENTMULTI Performs gradient descent to learn theta
+  ##   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters)
+  ##   updates theta by taking num_iters gradient steps with learning
+  ##   rate alpha
 
-
-
-
+  ## Initialize some useful values
+  J_history = zeros(num_iters, 1);
 
-
-
-
-
+  for iter = 1:num_iters
 
-
-    % ============================================================
+    J_history(iter) = computeCost(X, y, theta);
+    theta -= alpha*X'*(X*theta - y)/rows (y);
 
-    % Save the cost J in every iteration    
-    J_history(iter) = computeCostMulti(X, y, theta);
+  endfor
 
-end
-
-end
+endfunction