000 02379cam a2200349 a 4500
001 17212088
005 20220823111257.0
008 120315s2012 maua b 001 0 eng
010 _a 2012004558
020 _a9780262018029
_qhardcover.
040 _aDLC
_beng
_cDLC
_dDLC
_dEG-CaTKH
_erda
042 _apcc
050 0 0 _aQ325.5
_b.M87 2012
082 0 0 _a006.31 MU.M 2012
_223
100 1 _aMurphy, Kevin P.,
_d1970-
_eauthor.
245 1 0 _aMachine learning :
_ba probabilistic perspective /
_cKevin P. Murphy.
260 _aCambridge, MA :
_bMIT Press,
_cc2012.
264 1 _aCambridge, Mass. :
_bMIT Press,
_c2012
300 _axxix, 1067 pages :
_billustrations ;
_c24 cm.
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
490 0 _aAdaptive computation and machine learning series
504 _aIncludes bibliographical references (pages 1015-1045) and indexes.
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.
650 0 _aMachine learning.
650 0 _aProbabilities.
906 _a7
_bcbc
_corignew
_d1
_eecip
_f20
_gy-gencatlg
942 _2ddc
_cBK
998 _ayomna nassar
_bP
_d20220823
999 _c1203
_d1203