Book: Probabilistic Machine Learning for Civil Engineers

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The MIT Press

Table of Content & Introduction 
                         Table of Content
     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

How to cite

Goulet, J.-A. (2020), Probabilistic Machine Learning for Civil Engineers, The MIT press
[BibTeX] [EndNote]

Where to buy

The MIT press  
Amazon (US)
Amazon (CA)


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