Goulet, James-A., <br/><br/>
Probabilistic machine learning for civil engineers / 
James-A. Goulet. 
 - xxviii, 269 pages :  illustrations (some color) ;  26 cm 
<br/><br/>Includes bibliographical references (pages [259]-266) and index. 
<br/><br/> "The book introduces probabilistic machine learning concepts to civil engineering students and professionals, who typically do not have the background necessary to understand the subject from a purely computer science perspective. It presents key approaches among the three sub-fields of machine learning: supervised, unsupervised, and reinforcement learning. The methods are demonstrated through step-by-step examples and copius illustrations in order to simplify abstract concepts. The book will prepare readers to access the vast body of literature from the field of machine learning"-- 
<br/><br/> H.B 
<br/><br/><label>ISBN: </label>9780262538701 
<br/><br/><label>LCCN: </label>2019027152 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning.<br/>Probabilities.
<br/><br/><label>LC Class. No.: </label>Q325.5  / .G68 2020 
<br/><br/><label>Dewey Class. No.: </label>006.31  / GOU