gradientDescent.jl

function 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.
    #
    #println(size(X))
    #println(size(theta))
    #println(size(y))
    theta = theta - alpha*(X'*(X*theta-y))/m






    # ============================================================

    # Save the cost J in every iteration    
    J_history[iter] = computeCost(X, y, theta);

  end
  return (theta,J_history)
end