thông tin biểu ghi
ISBN 9780262018029
DDC 006.31
Tác giả CN Murphy, Kevin P.
Nhan đề Machine learning : a probabilistic perspective / Kevin P Murphy
Thông tin xuất bản Cambridge : Masschusetts, 2012
Mô tả vật lý xxix, 1071 p. : Illustrations ; 23 cm.
Tóm tắt "This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
Thuật ngữ chủ đề Probabilities
Thuật ngữ chủ đề Machine learning
Thuật ngữ chủ đề COMPUTERS -Enterprise Applications-Business Intelligence Tools.
Khoa Khoa Công nghệ Thông tin
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082 |a006.31|bM9781|223
100 |aMurphy, Kevin P.
245 |aMachine learning : |b a probabilistic perspective / |cKevin P Murphy
260 |aCambridge : |bMasschusetts, |c2012
300 |axxix, 1071 p. : |bIllustrations ; |c23 cm.
520 |a"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
541 |aMua
650 |aProbabilities
650 |aMachine learning
650 |aCOMPUTERS |vEnterprise Applications|xBusiness Intelligence Tools.
690 |aKhoa Công nghệ Thông tin
852|a300|bQ12_Kho Mượn_02|j(2): 068186-7
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