Store besparelser
Hurtig levering
Fri fragt over 499,-
Gemte
Log ind
0
Kurv
Bøger
Bestsellers
Kommer snart
Nyheder
Store besparelser
Fri fragt over 499,-
Hjem
Kategorier
Bestsellers
Kommer snart
Nyheder
FAQ
Konto
Kontakt
Kurv
  1. Bog
  2. Matematisk datateori
  3. Matematik til informatikfag
A Computational Approach to Statistical Learning
Af: Michael Kane, Taylor Arnold, Bryan W. Lewis
Engelsk
Bogcover for A Computational Approach to Statistical Learning af Michael Kane, Taylor Arnold, Bryan W. Lewis, 9780367570613
Specifikationer
Sprog:
Engelsk
Sider:
362
ISBN-13:
9780367570613
Indbinding:
Paperback
ISBN-10:
0367570610
Kategori:
Udg. Dato:
30 jun 2020
Størrelse i cm:
15,5 x 23,4 x 3,0
Forlag:
Serie:
Oplagsdato:
30 jun 2020
Forfatter(e):
Genre:

A Computational Approach to Statistical Learning

Af: Michael Kane, Taylor Arnold, Bryan W. Lewis
Engelsk
Paperback 2020
Format:
Paperback
Bog beskrivelse

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.

The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

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.
... Vis mere
Forlags Vejl. pris
570,84 kr
Hos Booktok
499 kr
spar 13%
Læg i kurv nu
Sikker betaling
23 - 25 hverdage
Specifikationer
Sprog:
Engelsk
Sider:
362
ISBN-13:
9780367570613
Indbinding:
Paperback
ISBN-10:
0367570610
Kategori:
Udg. Dato:
30 jun 2020
Størrelse i cm:
15,5 x 23,4 x 3,0
Forlag:
Serie:
Oplagsdato:
30 jun 2020
Forfatter(e):
Finder produkter...
Kategori sammenhænge