000			01881nam a22002417a 4500
003			OSt
005			20251203120625.0
008			251203b |||||||| |||| 00| 0 eng d
020			_a9780262039406
040			_cSoET Library
082			_a006.31 MOH
100			_aMohri, Mehryar _92963
245			_aFoundations of Machine Learnin
250			_a2nd
260			_aCambridge: _bThe MIT Press, _c2018.
300			_axv, 486 p. : ill.
520			_ahis 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.
650			_aMachine Learning _996
650			_aArtificial Intelligence _9100
700			_aRostamizadeh, Afshin _92964
700			_aTalwalkar, Ameet _92965
942			_2ddc _cBK
999			_c10129 _d10129