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Optimization techniques are at the core of data science, including
data analysis and machine learning. An understanding of basic
optimization techniques and their fundamental properties provides
important grounding for students, researchers, and practitioners in
these areas. This text covers the fundamentals of optimization
algorithms in a compact, self-contained way, focusing on the
techniques most relevant to data science. An introductory chapter
demonstrates that many standard problems in data science can be
formulated as optimization problems. Next, many fundamental methods
in optimization are described and analyzed, including: gradient and
accelerated gradient methods for unconstrained optimization of
smooth (especially convex) functions; the stochastic gradient
method, a workhorse algorithm in machine learning; the coordinate
descent approach; several key algorithms for constrained
optimization problems; algorithms for minimizing nonsmooth
functions arising in data science; foundations of the analysis of
nonsmooth functions and optimization duality; and the
back-propagation approach, relevant to neural networks.
This book focuses on ways to better manage and prevent
aircraft-based homicide events while in flight using alternate
technology to replace the Cockpit Voice Recorder (CVR) and/or
Digital Flight Data Recorder (DFDR) functions. While these events
are infrequent, the implementation of real-time predictive
maintenance allows aircraft operators to better manage both
scheduled and unscheduled maintenance events. Aviation Safety and
Security: Utilizing Technology to Prevent Aircraft Fatality
explores historical events of in-flight homicide and includes
relevant accident case study excerpts from the National
Transportation Safety Board (NTSB) and Air Accidents Investigation
Branch (AAIB). FEATURES Explores historical events of in-flight
homicide and offers solutions for ways to mitigate risk Explains
how alternate technologies can be implemented to address in-flight
safety issues Demonstrates that metrics for change are not solely
for safety but also for financial savings for aircraft operation
Includes relevant accident case study excerpts from the NTSB and
AAIB Expresses the need for real-time predictive maintenance
Stephen J Wright is an academic Professor at the faculty of
Engineering and Natural Sciences at Tampere University, Finland,
specializing in aviation, aeronautical engineering, and aircraft
systems.
Creation or evolution? Did God speak everything into existence or
was it all made through natural processes? Does it really matter,
anyway? Dr. Jeff Duncan, the newly hired biology professor at
Grogan University, is about to discover the truth. The Genesis
Creation, Noah's Ark, the Tower of Babel...Jeff was taught all the
classic Bible stories as a child, and although he accepted them at
face value, he was never taught how to defend his faith, nor were
his Sunday school teachers able to provide answers to the many
questions he asked. Faced with a series of life-altering events,
Jeff is forced to choose between his childhood faith and the
evolutionary biology he learned in college and has now been hired
to teach. If the historical accounts in Genesis are not true, then
what about the rest of the Bible? Jeff begins to question
everything he had been taught as a child and almost denies his
faith altogether, but God has something else in store for him. Why
couldn't Jeff ever establish a firm foundation as a child and
understand his faith back then? Shouldn't the people who were
teaching him in church every Sunday be able to answer his
questions? Were his Sunday school teachers deceiving him all those
years by just telling about Bible "stories," or, now as a professor
of evolution, is Jeff part of...the deception?
An up-to-date account of the interplay between optimization and
machine learning, accessible to students and researchers in both
communities. The interplay between optimization and machine
learning is one of the most important developments in modern
computational science. Optimization formulations and methods are
proving to be vital in designing algorithms to extract essential
knowledge from huge volumes of data. Machine learning, however, is
not simply a consumer of optimization technology but a rapidly
evolving field that is itself generating new optimization ideas.
This book captures the state of the art of the interaction between
optimization and machine learning in a way that is accessible to
researchers in both fields. Optimization approaches have enjoyed
prominence in machine learning because of their wide applicability
and attractive theoretical properties. The increasing complexity,
size, and variety of today's machine learning models call for the
reassessment of existing assumptions. This book starts the process
of reassessment. It describes the resurgence in novel contexts of
established frameworks such as first-order methods, stochastic
approximations, convex relaxations, interior-point methods, and
proximal methods. It also devotes attention to newer themes such as
regularized optimization, robust optimization, gradient and
subgradient methods, splitting techniques, and second-order
methods. Many of these techniques draw inspiration from other
fields, including operations research, theoretical computer
science, and subfields of optimization. The book will enrich the
ongoing cross-fertilization between the machine learning community
and these other fields, and within the broader optimization
community.
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