02053cam a22002655a 4500 17212088 20201128021456.0 120315t2012 maua frb 001 0 eng d 9780262018029 (hardcover : alk. paper) DLC eng DLC EG-ScBUE EG-ScBUE 006.31 MUR 22 Murphy, Kevin P., 1970- 38852 Machine learning : a probabilistic perspective / Kevin P. Murphy. Cambridge, Massachusetts : Massachusetts Institute of Technology (The MIT Press) , c.2012. xxix, 1071 p. : ill. (some col.) ; 24 cm. Adaptive computation and machine learning Index : p.1051-1071. Bibliography : p. 1019- 1050. 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. Machine learning. BUEsh 2922 Probabilities. BUEsh 3494 BUEsh COMSCI August2015 December2015 January2016 ddc 006.31 MUR 20482 20454 0 0 ddc 0 0 Baccah MAIN MAIN LOW 2015-08-09 Purchase 612.00 21782 5 49 006.31 MUR 000030805 2025-07-15 00:00:00 2021-08-17 765.00 BB 0 0 ddc 0 0 Baccah MAIN MAIN LOW 2015-08-17 Purchase 612.00 21782 2 3 006.31 MUR 000030765 2025-07-15 00:00:00 2021-09-05 785.00 BB