# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning) epub download

### by Francis Bach,Kevin P. Murphy

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.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Machine learning provides these. by. Kevin P. Murphy (Author). Find all the books, read about the author, and more.

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oceedings{Murphy2012MachineL, title {Machine learning - a probabilistic perspective}, author {Kevin P. Murphy}, booktitle {Adaptive computation and machine learning series}, year {2012} }. Murphy. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data.

Murphy, Kevin P. Machine learning : a probabilistic perspective, Kevin P. p. cm. - (Adaptive computation and machine learning series) Includes bibliographical references and index. ISBN 978-0-262-01802-9 (hardcover : alk. paper) 1. Machine learning.

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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. Previously, he was Associate Professor of Computer Science and Statistics at the University of British Columbia. Informazioni bibliografiche. Machine Learning: A Probabilistic Perspective Adaptive Computation and Machine Learning series.

Machine Learning book. Adaptive Computation and Machine Learning). A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Machine Learning: A Probabilistic Perspective. 1098 Pages · 2012 · 2. 9 MB · 4,804 Downloads ·English. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning. 59 MB·43,576 Downloads·New! This practical guide provides nearly 200 self-contained recipes to help you solve machine learning. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques. 31 MB·32,943 Downloads·New!

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series).

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). By 作者: Kevin P.

Items related to Machine Learning: A Probabilistic Perspective (Adaptive. Murphy, Kevin P. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). ISBN 13: 9780262018029.

**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.

**ISBN13:** 978-0262018029

**ISBN:** 0262018020

**Author: ** Francis Bach,Kevin P. Murphy

**Category: ** Computers and Technology

**Subcategory: ** Computer Science

**Language: ** English

**Publisher: ** The MIT Press; 1 edition (August 24, 2012)

**Pages: ** 1104 pages

**ePUB size:** 1552 kb

**FB2 size:** 1919 kb

**Rating: ** 4.8

**Votes: ** 357

**Other Formats: ** azw rtf lrf docx