In Andrew Ng's Machine Learning class, the first section demonstrates gradient descent by using it on a familiar problem, that of fitting a linear function to data. Let's start off, by generating some bogus data with known characteristics. Let's make y just a noisy version of x. Let's also add 3 to give the intercept term something to...