{"product_id":"data-science-and-machine-learning-mathematical-and-statistical-methods-second-edition","title":"Data Science and Machine Learning Mathematical and Statistical Methods, Second Edition","description":"\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 Majorization-Minimization method 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","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":48104630354110,"sku":null,"price":128.24,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0536\/0592\/5054\/files\/16493393482555.jpg?v=1783621131","url":"https:\/\/bookshopnow.com\/products\/data-science-and-machine-learning-mathematical-and-statistical-methods-second-edition","provider":"Book Shop Now","version":"1.0","type":"link"}