warmUpExercise.m

A = eye(5);

plotData.m

plot(x, y, 'rx', 'MarkerSize', 10);
ylabel('Profit in $10,000s'); 
xlabel('Population of City in 10,000s');

gradientDescent.m

plot(x, y, 'rx', 'MarkerSize', 10);
ylabel('Profit in $10,000s'); 
xlabel('Population of City in 10,000s');

computeCost.m

predictions = X * theta;
sqrErrors = (predictions - y) .^ 2;
J = 1 / (2 * m) * sum(sqrErrors)

gradientDescentMulti.m

S = (1 / m) * (X' * (X * theta - y));
theta = theta - alpha .* S;

computeCostMulti.m

J = 1 / (2 * m) * sum( (X * theta - y) .^ 2);

featureNormalize.m

mu = mean(X);
sigma = std(X, 1, 1);

for i = 1:size(X, 2)
    X_norm(:, i) = (X(:, i) - mu(i)) ./ sigma(i);
end;

normalEqn.m

theta = pinv((X'*X))*X'*y;