{"product_id":"understanding-machine-learning-from-theory-to-algorithms-hardback-9781107057135","title":"Understanding Machine Learning; From Theory to Algorithms (Hardback) 9781107057135","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eUnderstanding Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFrom Theory to Algorithms\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eIntroduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eShai Shalev-Shwartz (Author), Shai Ben-David (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781107057135, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 May 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e410 pages, 47 b\/w illus.  123 exercises\u003cbr\u003e26 x 18.3 x 2.8 cm, 0.91 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'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, Berkeley\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eMachine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e Part I. Foundations: 2. A gentle start\u003cbr\u003e 3. A formal learning model\u003cbr\u003e 4. Learning via uniform convergence\u003cbr\u003e 5. The bias-complexity trade-off\u003cbr\u003e 6. The VC-dimension\u003cbr\u003e 7. Non-uniform learnability\u003cbr\u003e 8. The runtime of learning\u003cbr\u003e Part II. From Theory to Algorithms: 9. Linear predictors\u003cbr\u003e 10. Boosting\u003cbr\u003e 11. Model selection and validation\u003cbr\u003e 12. Convex learning problems\u003cbr\u003e 13. Regularization and stability\u003cbr\u003e 14. Stochastic gradient descent\u003cbr\u003e 15. Support vector machines\u003cbr\u003e 16. Kernel methods\u003cbr\u003e 17. Multiclass, ranking, and complex prediction problems\u003cbr\u003e 18. Decision trees\u003cbr\u003e 19. Nearest neighbor\u003cbr\u003e 20. Neural networks\u003cbr\u003e Part III. Additional Learning Models: 21. Online learning\u003cbr\u003e 22. Clustering\u003cbr\u003e 23. Dimensionality reduction\u003cbr\u003e 24. Generative models\u003cbr\u003e 25. Feature selection and generation\u003cbr\u003e Part IV. Advanced Theory: 26. Rademacher complexities\u003cbr\u003e 27. Covering numbers\u003cbr\u003e 28. Proof of the fundamental theorem of learning theory\u003cbr\u003e 29. Multiclass learnability\u003cbr\u003e 30. Compression bounds\u003cbr\u003e 31. PAC-Bayes\u003cbr\u003e Appendix A. Technical lemmas\u003cbr\u003e Appendix B. Measure concentration\u003cbr\u003e Appendix C. Linear algebra.\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":45999651127576,"sku":"9781107057135","price":46.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781107057135i_a821b54e-2d43-4d19-b258-78d5a2fc0e68.jpg?v=1691361657","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/understanding-machine-learning-from-theory-to-algorithms-hardback-9781107057135","provider":"Freshly Printed Books","version":"1.0","type":"link"}