| Titre : | Machine learning : a probabilistic perspective |
| Auteurs : | K. Murphy, Auteur |
| Type de document : | ouvrage |
| Editeur : | MIT Press, 2012 |
| Collection : | Adaptative computation and machine learning series |
| ISBN/ISSN/EAN : | 978-0-262-01802-9 |
| Format : | 1 vol. (XXIX-1071 p.). / ill. en noir et en coul., couv. ill. en coul. / 24 cm |
| Langues: | = Anglais |
| Mots-clés: | APPRENTISSAGE ; MODELE PARAMETRIQUE ; STATISTIQUE BAYESIENNE ; REGRESSION ; MODELE GRAPHIQUE ; REGULARISATION ; MODELE LINEAIRE ; VARIABLE LATENTE ; MCMC ; AGREGATION ; METHODE VARIATIONNELLE |
| Résumé : | 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. |
Exemplaires (2)
| Centre | Localisation | Section | Cote | Statut | Disponibilité |
|---|---|---|---|---|---|
| PACA | Biostatistique et Processus Spatiaux | Ouvrages | BM-AV IA 028 | Consultable sur place | Exclu du prêt |
| PACA | Biostatistique et Processus Spatiaux | Ouvrages | BM-AV IA029 | Consultable sur place | Exclu du prêt |
