  Many machine learning problems and methods are combinations of three
components: data, hypothesis space and loss function. Different machine
learning methods are obtained as combinations of different choices for the
representation of data, hypothesis space and loss function. After reviewing the
mathematical structure of these three components, we discuss intrinsic
trade-offs between statistical and computational properties of machine learning
methods.
