  Big data and machine learning are driving comprehensive economic and social
transformations and are rapidly re-shaping the toolbox and the methodologies of
applied scientists. Machine learning tools are designed to learn functions from
data with little to no need of prior knowledge. As continuous developments in
experimental and numerical methods improve our ability to collect high-quality
data, machine learning tools become increasingly viable and promising also in
disciplines rooted in physical principles. These notes explore how machine
learning can be integrated and combined with more classic methods in fluid
dynamics. After a brief review of the machine learning landscape, we show how
many problems in fluid mechanics can be framed as machine learning problems and
we explore challenges and opportunities. We consider several relevant
applications: aeroacoustic noise prediction, turbulence modelling,
reduced-order modelling and forecasting, meshless integration of (partial)
differential equations, super-resolution and flow control. While this list is
by no means exhaustive, the presentation will provide enough concrete examples
to offer perspectives on how machine learning might impact the way we do
research and learn from data.
