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portada Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series)
Type
Physical Book
Publisher
Author
Language
English
Format
Hardcover
ISBN
0262018020
ISBN13
9780262018029
Categories

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series)

Murphy Kevin P. (Author) · Mit Pr · Hardcover

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) - Murphy Kevin P.

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Synopsis "Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) "

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.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. 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Customers reviews

Sebastian Alvarado Friday, May 14, 2021
Verified Purchase

Un muy buen libro, definitivamente algo que se debe tener en tu biblioteca si te interesa el tema. El contenido definitivamente justifica su precio.

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