000			02450nam a2200253Ia 4500
003			NU
005			20240429134216.0
008			230620s9999 xx 000 0 und d
020			_a978-0-367-57061-3
040			_aNUFAIRVIEW _cNUFAIRVIEW
050			_aQ 325.5 A76 2019
100			_aArnold, Taylor _eauthor
245		2	_aA computational approach to statistical learning / _cTaylor Arnold, Michael Kane and Bryan W. Lewis
260			_aBoca Raton, FL : _bCRC Press, _cc2019.
300			_axiii, 361 pages : _billustration ; _c24 cm.
504			_aIncludes bibliographical references and index.
505			_a Introduction Linear models Ridge regression and principal component analysis Linear smoothers Generalized linear models Additive models Penalized regression models Neural networks Dimensionality reduction Computation in practice Linear algebra and matrices Floating point arithmetic and numerical computation
520			_aA 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. -- _bProvided by publisher
650			_aMACHINE LEARNING -- MATHEMATICS.
650			_aMATHEMATICAL STATISTICS. ESTIMATION THEORY.
700			_aKane , Michael _eauthor
700			_aLewis Bryan W. _eauthor
942			_2lcc _cBK _n0
999			_c3888 _d3888