Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex5-008/mlclass-ex5/trainLinearReg.m

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+function [theta] = trainLinearReg(X, y, lambda)
+%TRAINLINEARREG Trains linear regression given a dataset (X, y) and a
+%regularization parameter lambda
+%   [theta] = TRAINLINEARREG (X, y, lambda) trains linear regression using
+%   the dataset (X, y) and regularization parameter lambda. Returns the
+%   trained parameters theta.
+%
+
+% Initialize Theta
+initial_theta = zeros(size(X, 2), 1); 
+
+% Create "short hand" for the cost function to be minimized
+costFunction = @(t) linearRegCostFunction(X, y, t, lambda);
+
+% Now, costFunction is a function that takes in only one argument
+options = optimset('MaxIter', 200, 'GradObj', 'on');
+
+% Minimize using fmincg
+theta = fmincg(costFunction, initial_theta, options);
+
+end