Suppose you have a dataset with m = 50 examples and n = 15 features for each example. You want to use multivariate linear regression to fit the parameters theta to our data. Should you prefer gradient descent or the normal equation and why?
If you can, it is preferable to use the normal equation to estimate the coefficients for multivariate linear regression. Since the normal equation is a closed-form expression, it will be faster than gradient descent.
Given you have relatively few examples and features, inverting the matrix is not an issue.