{"product_id":"a-computational-approach-to-statistical-learning-paperback","title":"A Computational Approach to Statistical Learning - Paperback","description":"\u003cp\u003eby \u003cb\u003eTaylor Arnold\u003c\/b\u003e (Author), \u003cb\u003eMichael Kane\u003c\/b\u003e (Author), \u003cb\u003eBryan W. Lewis\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eA Computational Approach to Statistical Learning\u003c\/em\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eTaylor 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, \u003ci\u003eHumanities Data in R\u003c\/i\u003e, was published in 2015.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMichael 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 \u003ci\u003ebigmemory\u003c\/i\u003e won the Chamber's prize for statistical software in 2010.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eBryan Lewis is an applied mathematician and author of many popular R packages, including \u003ci\u003eirlba\u003c\/i\u003e, \u003ci\u003edoRedis\u003c\/i\u003e, and \u003ci\u003ethreejs\u003c\/i\u003e.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTaylor Arnold\u003c\/strong\u003e 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, \u003ci\u003eHumanities Data in R\u003c\/i\u003e, was published in 2015. \u003c\/p\u003e\u003cp\u003eMichael 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 \u003ci\u003ebigmemory\u003c\/i\u003e won the Chamber's prize for statistical software in 2010.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eBryan Lewis is an applied mathematician and author of many popular R packages, including \u003ci\u003eirlba\u003c\/i\u003e, \u003ci\u003edoRedis\u003c\/i\u003e, and \u003ci\u003ethreejs\u003c\/i\u003e.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 362\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.78 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 30, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42709545222207,"sku":"9780367570613","price":134.11,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/83ee3a40d3992de35ae3ccf784488ba1.webp?v=1765051819","url":"https:\/\/dhlswag.com\/products\/a-computational-approach-to-statistical-learning-paperback","provider":"BBB","version":"1.0","type":"link"}