  Decision making algorithms are used in a multitude of different applications.
Conventional approaches for designing decision algorithms employ principled and
simplified modelling, based on which one can determine decisions via tractable
optimization. More recently, deep learning approaches that use highly
parametric architectures tuned from data without relying on mathematical
models, are becoming increasingly popular. Model-based optimization and
data-centric deep learning are often considered to be distinct disciplines.
Here, we characterize them as edges of a continuous spectrum varying in
specificity and parameterization, and provide a tutorial-style presentation to
the methodologies lying in the middle ground of this spectrum, referred to as
model-based deep learning. We accompany our presentation with running examples
in super-resolution and stochastic control, and show how they are expressed
using the provided characterization and specialized in each of the detailed
methodologies. The gains of combining model-based optimization and deep
learning are demonstrated using experimental results in various applications,
ranging from biomedical imaging to digital communications.
