Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex1-008/mlclass-ex1/gradientDescent.m

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+function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
+%GRADIENTDESCENT Performs gradient descent to learn theta
+%   theta = GRADIENTDESENT(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 (computeCost) and gradient here.
+    %
+
+
+	
+	theta = theta - alpha * (X' * (X*theta-y) / m);
+
+
+
+    % ============================================================
+
+    % Save the cost J in every iteration    
+    J_history(iter) = computeCost(X, y, theta);
+
+end
+
+end