Probabilistic Data-Driven Modeling by Tomaso Aste (.PDF)
File Size: 38.9 MB
Probabilistic Data-Driven Modeling by Tomaso Aste
Requirements: .PDF reader, 38.9 MB
Overview: This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools useful to individuate regularities, discover patterns and laws in complex datasets, and demonstrates how to apply them to devise models that help to understand these systems and predict their behaviors. By focusing on the estimation of multivariate probabilities, the book shows that the entire domain, from linear regressions to Deep Learning neural networks, can be formulated in probabilistic terms. This book provides the right balance between accessibility and mathematical rigor for applied Data Science or operations research students, graduate students in CSE, and Machine Learning and uncertainty quantification researchers who use statistics in their field. Background in probability theory and undergraduate mathematics is assumed. The book has a large body of supporting material consisting of data, examples, Python and Matlab codes which are provided on the GitHub page.
Genre: Non-Fiction > Tech & Devices
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