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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Probabilistic Machine Learning for Civil Engineers (Paperback)
Loot Price: R1,150
Discovery Miles 11 500
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Probabilistic Machine Learning for Civil Engineers (Paperback)
Series: The MIT Press
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An introduction to key concepts and techniques in probabilistic
machine learning for civil engineering students and professionals;
with many step-by-step examples, illustrations, and exercises. This
book introduces probabilistic machine learning concepts to civil
engineering students and professionals, presenting key approaches
and techniques in a way that is accessible to readers without a
specialized background in statistics or computer science. It
presents different methods clearly and directly, through
step-by-step examples, illustrations, and exercises. Having
mastered the material, readers will be able to understand the more
advanced machine learning literature from which this book draws.
The book presents key approaches in the three subfields of
probabilistic machine learning: supervised learning, unsupervised
learning, and reinforcement learning. It first covers the
background knowledge required to understand machine learning,
including linear algebra and probability theory. It goes on to
present Bayesian estimation, which is behind the formulation of
both supervised and unsupervised learning methods, and Markov chain
Monte Carlo methods, which enable Bayesian estimation in certain
complex cases. The book then covers approaches associated with
supervised learning, including regression methods and
classification methods, and notions associated with unsupervised
learning, including clustering, dimensionality reduction, Bayesian
networks, state-space models, and model calibration. Finally, the
book introduces fundamental concepts of rational decisions in
uncertain contexts and rational decision-making in uncertain and
sequential contexts. Building on this, the book describes the
basics of reinforcement learning, whereby a virtual agent learns
how to make optimal decisions through trial and error while
interacting with its environment.
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