But the intention here is not to sing the praises of these two methods or to elevate them above the many other techniques in machine learning. In fact, one of the major theoretical developments of the past twenty years has been a clearer realization of how superficially disparate techniques can be understood as special cases within a common mathematical framework. For example, many types of artificial neural network can be viewed as classifiers that perform a particular kind of statistical calculation (maximum likelihood estimation).
