Machine learning : a probabilistic perspective / Kevin P. Murphy.
Tipo de material: TextoSeries Adaptive computation and machine learningEditor: Cambridge, Mass. : MIT Press, [2012]Fecha de copyright: ©2012Descripción: xxix, 1071 páginas : ilustraciones (algunas a color) ; 24 cmTipo de contenido:- texto
- sin mediación
- volumen
- 9780262018029
- 0262018020
- Q 325.5 M87.2012
Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Copia número | Estado | Fecha de vencimiento | Código de barras | |
---|---|---|---|---|---|---|---|---|
Libros | Biblioteca Francisco Xavier Clavigero Acervo | Acervo General | Q 325.5 M87.2012 (Navegar estantería(Abre debajo)) | ej. 1 | Prestado | 2024-05-27 | UIA196254 |
Incluye referencias bibliográficas (páginas 1015-1045) e índice.
"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"--disponible gratuitamente en línea--cubierta posterior.