This chapter provides a high-level overview of machine learning, in particular how it is related to building models from data. It starts with placing the basic concept in its historical context and phrases the learning problem in a simple mathematical term as function approximation as well as in a probabilistic context. In contrast to more traditional models, machine learning can be characterized as non-linear regression in high-dimensional spaces. This chapter points out how diverse subareas such as deep learning and Bayesian networks fit into the scheme of things and motivates further study with some examples of recent progress.
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