So far, many ad hoc techniques have been proposed to compute maxium likelihood estimates for various specific models. Some work well, some don't. Our purpose in this presentation is to present a general approach based on quadratic majorization. This class of algorithms has the desirable property that it computes maximum likelihood estimates by solving a sequence of least squares problems, which are generally much simpler. It also produces an algorithm which is globally convergent.