  Machine learning is finding increasingly broad application in the physical
sciences. This most often involves building a model relationship between a
dependent, measurable output and an associated set of controllable, but
complicated, independent inputs. We present a tutorial on current techniques in
machine learning -- a jumping-off point for interested researchers to advance
their work. We focus on deep neural networks with an emphasis on demystifying
deep learning. We begin with background ideas in machine learning and some
example applications from current research in plasma physics. We discuss
supervised learning techniques for modeling complicated functions, beginning
with familiar regression schemes, then advancing to more sophisticated deep
learning methods. We also address unsupervised learning and techniques for
reducing the dimensionality of input spaces. Along the way, we describe methods
for practitioners to help ensure that their models generalize from their
training data to as-yet-unseen test data. We describe classes of tasks --
predicting scalars, handling images, fitting time-series -- and prepare the
reader to choose an appropriate technique. We finally point out some
limitations to modern machine learning and speculate on some ways that
practitioners from the physical sciences may be particularly suited to help.
