Machine learning : a probabilistic perspective
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and sel...
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Livre papier |
| Lingua: | Anglais |
| Pubblicazione: |
Cambridge (Mass.) ; London :
The MIT Press
C 2012.
|
| Serie: | Adaptive computation and machine learning
|
| Soggetti: | |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Machine learning, a probabilistic perspective, Kevin P. Murphy, 2012, Cambridge, Massachusetts, The MIT Press, Adaptive computation and machine learning series, 978-0-262-30524-2 |
| Riassunto: | Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. 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. |
|---|---|
| Descrizione fisica: | 1 vol. (xxix-1071 p.). : ill. en noir et en coul., couv. ill. en coul. ; 24 cm. |
| Bibliografia: | Bibliogr. p. [1019]-1050. Index |
| ISBN: | 9780262018029 (rel.) : 0262018020 (rel.) |