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.
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.
"As best as I can determine, ‘A Computational Approach to
Statistical Learning’ (CASL) is unique among R books devoted to
statistical learning and data science. Other popular texts…cover
much of the same ground, and include extensive R code implementing
statistical models. What makes CASL different is the unifying
mathematical structure underlying the presentation and the focus on
the computations themselves…CASL’s great strengths are the use
linear algebra to provide a coherent, unifying mathematical
framework for explaining a wide class of models, a lucid writing
style that appeals to geometric intuition, clear explanations of
many details that are mostly glossed over in more superficial
treatments, the inclusion of historical references, and R code that
is tightly integrated into the text. The R code is extensive,
concise without being opaque, and in many cases, elegant. The code
illustrates R’s advantages for developing statistical algorithms as
well as its power to present versatile and compelling
visualizations…CASL ought to appeal to anyone working in data
science or machine learning seeking a sophisticated understanding
of both the theoretical basis and efficient algorithms underlying a
modern approach to computational statistics."
~Joe Rickert, RStudio"Machine learning books tend to come in three
types: those that focus on theory and the underlying mathematics,
those that develop well-known algorithms ‘from scratch’ to
illustrate how they work, and those that take a ‘hands-on’approach
and apply methods from standard libraries to real data. This book
has the perfect balance of all three... The book is very well
written and suitable for both self-study and as a course
text."Stanley E. Lazic, Prioris.ai Inc, Canada, Royal Statistical
Society, Series A Statistics in Society, July 2021.
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