For our purposes, we’ll view machine learning as a form of statistical discrimination, where the “machine” does the heavy lifting. That is, the computer “learns” important information, saving us humans from the hard work of trying to extract useful information from seemingly inscrutable data. For the applications considered in this book, we typically train a model, then use the resulting model to score samples. If the score is sufficiently high, we classify the sample as being of the same type as was used to train the model. And thanks to the miracle of machine learning, we don’t have to work too hard to perform such classification. Since the model parameters are (more-or-less) automatically extracted from training data, machine learning algorithms are sometimes said to be data driven.