This book is about the growing intersection of data-driven methods, applied optimization,
and the classical fields of engineering mathematics and mathematical physics. We have
been developing this material over a number of years, primarily to educate our advanced
undergrad and beginning graduate students from engineering and physical science depart-
ments. Typically, such students have backgrounds in linear algebra, differential equations,
and scientific computing, with engineers often having some exposure to control theory
and/or partial differential equations. However, most undergraduate curricula in engineering
and science fields have little or no exposure to data methods and/or optimization. Likewise,
computer scientists and statisticians have little exposure to dynamical systems and control.
Our goal is to provide a broad entry point to applied data science for both of these groups
of students. We have chosen the methods discussed in this book for their (1) relevance,
(2) simplicity, and (3) generality, and we have attempted to present a range of topics, from
basic introductory material up to research-level techniques.
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