{"product_id":"bayesian-reasoning-and-machine-learning-hardback-9780521518147","title":"Bayesian Reasoning and Machine Learning (Hardback) 9780521518147","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eBayesian Reasoning and Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eA practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDavid Barber (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521518147, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 February 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e735 pages, 287 b\/w illus.  1 table  260 exercises\u003cbr\u003e25.1 x 19.3 x 3.7 cm, 1.71 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'I repeatedly get unsolicited comments from my students that the contents of this book have been very valuable in developing their understanding of machine learning … My students praise this book because it is both coherent and practical, and because it makes fewer assumptions regarding the reader's statistical knowledge and confidence than many books in the field.' Amos Storkey, University of Edinburgh\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eMachine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning\u003cbr\u003e 2. Basic graph concepts\u003cbr\u003e 3. Belief networks\u003cbr\u003e 4. Graphical models\u003cbr\u003e 5. Efficient inference in trees\u003cbr\u003e 6. The junction tree algorithm\u003cbr\u003e 7. Making decisions\u003cbr\u003e Part II. Learning in Probabilistic Models: 8. Statistics for machine learning\u003cbr\u003e 9. Learning as inference\u003cbr\u003e 10. Naive Bayes\u003cbr\u003e 11. Learning with hidden variables\u003cbr\u003e 12. Bayesian model selection\u003cbr\u003e Part III. Machine Learning: 13. Machine learning concepts\u003cbr\u003e 14. Nearest neighbour classification\u003cbr\u003e 15. Unsupervised linear dimension reduction\u003cbr\u003e 16. Supervised linear dimension reduction\u003cbr\u003e 17. Linear models\u003cbr\u003e 18. Bayesian linear models\u003cbr\u003e 19. Gaussian processes\u003cbr\u003e 20. Mixture models\u003cbr\u003e 21. Latent linear models\u003cbr\u003e 22. Latent ability models\u003cbr\u003e Part IV. Dynamical Models: 23. Discrete-state Markov models\u003cbr\u003e 24. Continuous-state Markov models\u003cbr\u003e 25. Switching linear dynamical systems\u003cbr\u003e 26. Distributed computation\u003cbr\u003e Part V. Approximate Inference: 27. Sampling\u003cbr\u003e 28. Deterministic approximate inference\u003cbr\u003e Appendix. Background mathematics\u003cbr\u003e Bibliography\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\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":46000834871576,"sku":"9780521518147","price":59.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521518147i_c49e7e84-8a94-4020-a0ee-80e8e137ce04.jpg?v=1691383495","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/bayesian-reasoning-and-machine-learning-hardback-9780521518147","provider":"Freshly Printed Books","version":"1.0","type":"link"}