000			02337nam a22002657a 4500
005			20240402112413.0
008			240130b |||||||| |||| 00| 0 eng d
020			_a9780262039406
028			_a: Raghav Books _b: A-184, A, Nand Gram, Ghaziabad, UP. _c: 4838 _d: 20/01/2024
082			_a006.31 MOH
100			_aMohri, Mehryar.
100			_a; Rostamizadeh, Afshin.
245			_aFoundations of Machine Learning
250			_a: 2nd.
260			_a, London _b: MIT Press _c, 2018.
300			_axii,486p. _c: 28cm.
365			_b5153
520			_aThis 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			_aMachine Learning
650			_aAlgorithms
650			_aSupport Vector Machines
650			_aVC-dimension
700			_aMehryar Mohri
942			_cBOOK
999			_c61408 _d61408