Probabilistic Machine Learning for Civil Engineers

✍ Scribed by James-A Goulet

• Publisher: The MIT Press
• Year: April 14,
• Tongue: English
• Leaves: 236
• Edition: Illustrated
• Category: Library

✦ Synopsis

Summary

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.

✦ Table of Contents

Table of Content

Nomenclature
Chapter 1: Introduction

Part one: Background
Chapter 2: Linear Algebra
Chapter 3: Probability Theory
Chapter 4: Probability Distributions
Chapter 5: Convex Optimization

Part two: Bayesian Estimation
Chapter 6: Learning from Data
Chapter 7: Markov Chain Monte Carlo

Part three: Supervised Learning
Chapter 8: Regression
Chapter 9: Classification

Part four: Unsupervised Learning
Chapter 10: Clustering
Chapter 11: Bayesian Networks
Chapter 12: State-Space Models
Chapter 13: Model Calibration

Part five: Reinforcement Learning
Chapter 14: Decision in Uncertain Contexts
Chapter 15: Sequential Decisions

✦ Subjects

Machine learning, Artificial Intelligence, Civil Engineers, Civil Engineering, Big Data, Monitoring, Sensors, Probability, Bayesian Probability