  Statistical learning is the process of estimating an unknown probabilistic
input-output relationship of a system using a limited number of observations. A
statistical learning machine (SLM) is the algorithm, function, model, or rule,
that learns such a process; and machine learning (ML) is the conventional name
of this field. ML and its applications are ubiquitous in the modern world.
Systems such as Automatic target recognition (ATR) in military applications,
computer aided diagnosis (CAD) in medical imaging, DNA microarrays in genomics,
optical character recognition (OCR), speech recognition (SR), spam email
filtering, stock market prediction, etc., are few examples and applications for
ML; diverse fields but one theory. In particular, ML has gained a lot of
attention in the field of cyberphysical security, especially in the last
decade. It is of great importance to this field to design detection algorithms
that have the capability of learning from security data to be able to hunt
threats, achieve better monitoring, master the complexity of the threat
intelligence feeds, and achieve timely remediation of security incidents. The
field of ML can be decomposed into two basic subfields: \textit{construction}
and \textit{assessment}. We mean by \textit{construction} designing or
inventing an appropriate algorithm that learns from the input data and achieves
a good performance according to some optimality criterion. We mean by
\textit{assessment} attributing some performance measures to the constructed ML
algorithm, along with their estimators, to objectively assess this algorithm.
\textit{Construction} and \textit{assessment} of a ML algorithm require
familiarity with different other fields: probability, statistics, matrix
theory, optimization, algorithms, and programming, among others.f
