Probabilistic Machine Learning for Civil Engineers by James A. Goulet
Probabilistic Machine Learning for Civil Engineers - Table of Contents
1. Introduction
Part I: Background
2. Linear Algebra
3. Probability Theory
4. Probability Distributions
5. Convex Optimization
Part II: Bayesian Estimation
6. Learning from Data
7. Markov Chain Monte Carlo
Part III: Supervised Learning
8. Regression
9. Classification
Part IV: Unsupervised Learning
10. Clustering
11. Bayesian Networks
12. State-Space Models
13. Model Calibration
Part V: Reinforcement Learning
14. Decision in Uncertain Contexts
15. Sequential Decisions
What You Will Learn in Probabilistic Machine Learning for Civil Engineers
Probabilistic Machine Learning for Civil Engineers by James-A. Goulet is a modern textbook that introduces how machine learning and probability theory are applied in civil engineering. It focuses on understanding uncertainty in engineering systems and using data-driven methods to improve decision-making in real-world infrastructure problems. The book is aimed at students, researchers, and professionals working in computational and structural engineering.
The book explains key concepts such as Bayesian inference, Gaussian processes, uncertainty quantification, and statistical modeling. It shows how these probabilistic tools can replace or enhance traditional deterministic engineering methods. Readers also learn how to apply these techniques in areas like structural health monitoring, sensor data analysis, and reliability assessment of infrastructure systems.
A major strength of this book is its strong focus on real engineering applications. It connects applied probability, data science, and civil engineering practice through practical examples and case studies. Overall, it helps readers understand how to model uncertainty, improve predictions, and make better engineering decisions using modern probabilistic machine learning methods.
Book Details & Specifications
Title:
Probabilistic Machine Learning for Civil Engineers by James A. Goulet
Publisher:
The MIT Press
Year:
2020
Pages:
300
Type:
PDF
Language:
English
ISBN-10 #:
0262538709
ISBN-13 #:
978-0262538701
License:
CC BY-NC-ND 4.0
Amazon:
Amazon
About the Author: James A. Goulet
The author
James A. Goulet
is a Canadian civil engineer and professor at EPFL (École Polytechnique Fédérale de Lausanne, Switzerland). He earned his PhD in Civil Engineering from the University of British Columbia, focusing on uncertainty quantification and Bayesian methods in engineering systems. His background combines civil engineering, applied mathematics, and data science.
His expertise includes probabilistic machine learning, Bayesian inference, structural health monitoring, and reliability analysis. He works on integrating data-driven models with engineering physics to improve decision-making in infrastructure systems, making him a leading researcher in modern civil engineering analytics.
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