Probabilistic Machine Learning for Civil Engineers (Mit Press)

Goulet, James-A.

ISBN 10: 0262538709 / ISBN 13: 9780262538701

Edité par The MIT Press, 2020

A propos de cet article

Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting. N° de réf. du vendeur MIT-PB-VG-0262538709

Détails bibliographiques

Titre : Probabilistic Machine Learning for Civil ...

Éditeur : The MIT Press

Date d'édition : 2020

Reliure : paperback

Etat : Very Good

Edition : Illustrated.

A propos de ce titre

Synopsis :

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.

À propos de l?auteur:

James-A. Goulet is Associate Professor of Civil Engineering at Polytechnique Montreal.