000			02960nam a2200241Ia 4500
003			NULRC
005			20250520102950.0
008			250520s9999 xx 000 0 und d
020			_a9780262039406
040			_cNULRC
050			_aQ 325.5 .M64 2018
100			_aMohri, Mehryar _eauthor
245		0	_aFoundations of machine learning / _cMehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
250			_aSecond edition
260			_aCambridge, Massachusetts : _bThe MIT Press, _cc2018
300			_axv,486 pages : _billustrations ; _c24 cm
504			_aIncludes bibliographical references and index.
505			_aIntroduction -- The PAC learning framework -- Rademacher complexity and VC-dimension -- Model selection -- Support vector machines -- Kernel methods - Boosting -- On-line learning -- Multi-class classification -- Ranking -- Regression -- Maximum entropy models -- Conditional maximum entropy models -- Algorithmic stability -- Dimensionality reduction -- Learning automata and languages -- Reinforcement learning -- Conclusion -- Appendices: Linear algebra review ; Convex optimization ; Probability review ; Concentration inequalities ; Notions of information theory.
520			_a"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--Provided by publisher.
650			_aMACHINE LEARNING
700			_aRostamizadeh, Afshin;Talwalkar, Ameet _eco-author;co-author
942			_2lcc _cBK
999			_c20033 _d20033