{"product_id":"foundations-of-data-science-hardback-9781108485067","title":"Foundations of Data Science (Hardback) 9781108485067","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eFoundations of Data Science\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eCovers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAvrim Blum (Author), John Hopcroft (Author), Ravindran Kannan (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781108485067, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 23 January 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e432 pages\u003cbr\u003e25.9 x 18.2 x 2.7 cm, 0.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'One plausible measure of [Foundations of Data Science's] impact is the book's own citation metrics. Semantic Scholar (https:\/\/www.semanticscholar.org) reports 81 citations with 42 citations related to background or methods; [Foundations of Data Science] appears to be on course to becoming influential.' M. Mounts, Choice\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e 2. High-dimensional space\u003cbr\u003e 3. Best-fit subspaces and Singular Value Decomposition (SVD)\u003cbr\u003e 4. Random walks and Markov chains\u003cbr\u003e 5. Machine learning\u003cbr\u003e 6. Algorithms for massive data problems: streaming, sketching, and sampling\u003cbr\u003e 7. Clustering\u003cbr\u003e 8. Random graphs\u003cbr\u003e 9. Topic models, non-negative matrix factorization, hidden Markov models, and graphical models\u003cbr\u003e 10. Other topics\u003cbr\u003e 11. Wavelets\u003cbr\u003e 12. Appendix.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46001013096728,"sku":"9781108485067","price":40.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781108485067i.jpg?v=1696706274","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/foundations-of-data-science-hardback-9781108485067","provider":"Freshly Printed Books","version":"1.0","type":"link"}