000			03514cam a2200313 i 4500
003			MIUC
005			20211026131114.0
008			211026s2018 maua b 001 0 eng
020			_a9780262039406 _q(hardcover : alk. paper)
040			_aDLC _beng _cDLC _erda _dDLC _dMIUC
082	0	0	_a006.31 _223
100	1		_aMohri, Mehryar _eauthor _95438
245	1	0	_aFoundations of machine learning / _cMehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar.
250			_aSecond edition.
264		1	_aCambridge, Massachusetts ; _aLondon, England : _bThe MIT Press, _c[2018].
300			_axv, 486 pages : _billustrations (some colour) ; _c24 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aAdaptive computation and machine learning
504			_aIncludes bibliographical references (pages 461-474) and index.
505	0		_a1. Introduction -- 2. The PAC Learning Framework -- 3. Rademacher Complexity and VC-Dimension -- 4. Model Selection -- 5. Support Vector Machines -- 6. Kernel Methods -- 7. Boosting -- 8. On-line Learning -- 9. Multi-Class Classification -- 10. Ranking -- 11. Regression -- 12. Maximum Entropy -- 13. Conditional Maximum Entropy Models -- 14. Algorithmic Stability -- 15. Dimensionality Reduction -- 16. Learning Automata and Language -- 17. Reinforcement Learning -- Conclusion -- A. Linear Algebra Review -- B. Convex Optimization -- C. Probability Review -- D. Concentration Inequalities -- E. Notions of Information Theory F. Notation.
520			_aA new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
650		0	_aMachine learning _95439
650		0	_aComputer algorithms _95440
700	1		_aRostamizadeh, Afshin _eauthor _95441
700	1		_aTalwalkar, Ameet _eauthor _95442
942			_2ddc _cBK