This chapter focuses on machine learning as a general way of thinking about the world, and provides a high-level characterization of the major goals of machine learning. Structural inference is the basis of many, and arguably most, machine learning frameworks and methods, including many well-known ones such as various forms of regression, neural-network learning algorithms such as back propagation, and causal learning algorithms using Bayesian networks. Machine learning algorithms must balance three factors: complexity of the learned model, which provides increased accuracy in representing the input dataset; generalizability of the learned model to new data, which enables the use of the model in novel contexts; and computational tractability of learning and using the model, which is a necessary precondition for the algorithms to have practical value. The practice of machine learning inevitably involves some human element to specify and control the algorithm, test various assumptions, and interpret the algorithm output.