This book synthesizes those techniques from numerical analysis, algorithms, data structures, and optimization theory mostcommonly employed in statistics and machine learning. We provide concrete applications of these methods by giving complete reference implementations for a large set of the most commonly used statistical estimators. The goal is
This book synthesizes those techniques from numerical analysis, algorithms, data structures, and optimization theory mostcommonly employed in statistics and machine learning. We provide concrete applications of these methods by giving complete reference implementations for a large set of the most commonly used statistical estimators. The goal is
Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.
Inhaltsangabe
Matrix Methods. Direct solutions to linear systems. Iterative linear model solutions. Iteratively reweighted least squares. Blockwise techniques. Convex optimization. Quasi-Newton and gradient descent. Interior point method. Proximal algorithms. Coordinate descent. Active sets and path solutions. Other techniques. Expectation maximization. Model featurization. Neighborhood prediction. Spectral learning. Stochastic techniques.
Matrix Methods. Direct solutions to linear systems. Iterative linear model solutions. Iteratively reweighted least squares. Blockwise techniques. Convex optimization. Quasi-Newton and gradient descent. Interior point method. Proximal algorithms. Coordinate descent. Active sets and path solutions. Other techniques. Expectation maximization. Model featurization. Neighborhood prediction. Spectral learning. Stochastic techniques.
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