Probabilistic Machine Learning for Civil Engineers  PDF & ePUB Download
ePUB
 eBook:Probabilistic Machine Learning for Civil Engineers
 Author:JamesA. Goulet
 Edition:
 Categories:
 Data:April 14, 2020
 ISBN:0262538709
 ISBN13:9780262538701
 Language:English
 Pages:304 pages
 Format:ePUB
Description of Probabilistic Machine Learning for Civil Engineers ebook
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, statespace models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decisionmaking 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.

Content
I  Background
3. Probability Theory
4. Probability Distributions
5. Convex Optimization
II  Bayesian Estimation
6. Learning from Data
7. Markov Chain Monte Carlo
III  Supervised Learning
8. Regression
9. Classification
IV  Unsupervised Learning
10. Clustering and Dimension Reduction
11. Bayesian Networks
12. StateSpace Models
13. Model Calibration
V  Reinforcement Learning
14. Decisions in Uncertain Contexts
15. Sequential Decisions
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