{"product_id":"data-science-and-machine-learning-mathematical-and-statistical-methods-second-edition-hardcover","title":"Data Science and Machine Learning: Mathematical and Statistical Methods, Second Edition - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eZdravko Botev\u003c\/b\u003e (Author), \u003cb\u003eDirk P. Kroese\u003c\/b\u003e (Author), \u003cb\u003eThomas Taimre\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePraise for the first edition: \u003c\/p\u003e\u003cp\u003e\"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.\"\u003c\/p\u003e\u003cp\u003e- Joacim Rocklöv and Albert A. Gayle, \u003ci\u003eInternational Journal of Epidemiology\u003c\/i\u003e, Volume 49, Issue 6\u003c\/p\u003e\u003cp\u003e\"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely--very useful for readers who wish to understand the rationale and flow of the background knowledge.\"\u003c\/p\u003e\u003cp\u003e- Yin-Ju Lai and Chuhsing Kate Hsiao, \u003ci\u003eBiometrics\u003c\/i\u003e, Volume 77, Issue 4\u003c\/p\u003e\u003cp\u003eThe purpose of \u003ci\u003eData Science and Machine Learning: Mathematical and Statistical Methods \u003c\/i\u003eis to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eNew in the Second Edition\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis expanded edition provides updates across key areas of statistical learning: \u003c\/p\u003e\u003cul\u003e \u003cli\u003e \u003cb\u003eMonte Carlo Methods\u003c\/b\u003e: A new section introducing \u003ci\u003eregenerative rejection sampling\u003c\/i\u003e - a simpler alternative to MCMC.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eUnsupervised Learning\u003c\/b\u003e: Inclusion of two multidimensional diffusion kernel density estimators, as well as the \u003ci\u003ebandwidth perturbation matching\u003c\/i\u003e method for the optimal data-driven bandwidth selection.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eRegression\u003c\/b\u003e: New automatic bandwidth selection for local linear regression.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eFeature Selection and Shrinkage\u003c\/b\u003e: A new chapter introducing the \u003ci\u003eklimax method\u003c\/i\u003e for model selection in high-dimensions.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eReinforcement Learning\u003c\/b\u003e: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eAppendices\u003c\/b\u003e: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel \u003cem\u003eMajorization\u003c\/em\u003e-\u003cem\u003eMinimization method\u003c\/em\u003e for constrained optimization.\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003cb\u003eKey Features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eFocuses on mathematical understanding.\u003c\/li\u003e \u003cli\u003ePresentation is self-contained, accessible, and comprehensive.\u003c\/li\u003e \u003cli\u003eExtensive list of exercises and worked-out examples.\u003c\/li\u003e \u003cli\u003eMany concrete algorithms with Python code.\u003c\/li\u003e \u003cli\u003eFull color throughout and extensive indexing.\u003c\/li\u003e \u003cli\u003eA single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.\u003c\/li\u003e \u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eZdravko I. Botev\u003c\/b\u003e, PhD, is the pioneer of several modern statistical methodologies, including the \u003ci\u003ediffusion kernel density estimator\u003c\/i\u003e, the \u003ci\u003egeneralized splitting method\u003c\/i\u003e for rare-event simulation, the \u003ci\u003ebandwidth perturbation matching\u003c\/i\u003e method, the \u003ci\u003eregenerative rejection sampling\u003c\/i\u003e method, and the \u003ci\u003eklimax method\u003c\/i\u003e for feature selection. His contributions to computational statistics and data science have been recognized with honours such as the Christopher Heyde Medal from the Australian Academy of Science and the Gavin Brown Prize from the Australian Mathematical Society.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDirk P. Kroese, PhD\u003c\/b\u003e, is an Emeritus Professor in Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThomas Taimre\u003c\/b\u003e, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley).\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 730\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.56 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 21, 2025\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43157026242623,"sku":"9781032488684","price":194.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/zSjVmE16ho9781032488684.webp?v=1776977408","url":"https:\/\/dhlswag.com\/products\/data-science-and-machine-learning-mathematical-and-statistical-methods-second-edition-hardcover","provider":"BBB","version":"1.0","type":"link"}