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Machine Learning: A Probabilistic Perspective
출판사 : MIT Press
저 자 : Murphy
ISBN : 9780262018029
발행일 : 2012-8
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 1104
판매가격 : 10,000원
판매여부 : 재고확인요망
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   Machine Learning: A Probabilistic Perspective 목차

This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.

British Computer Society
An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong and deep! A great starting point for any university student—and a must have for anybody in the field.

Jan PetersDarmstadt University of Technology; Max-Planck Institute for Intelligent Systems
Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data.

John WinnMicrosoft Research, Cambridge
This is a wonderful book that starts with basic topics in statistical modeling, culminating in the most advanced topics. It provides both the theoretical foundations of probabilistic machine learning as well as practical tools, in the form of Matlab code.The book should be on the shelf of any student interested in the topic, and any practitioner working in the field.

Yoram SingerGoogle Inc.
This book will be an essential reference for practitioners of modern machine learning. It covers the basic concepts needed to understand the field as whole, and the powerful modern methods that build on those concepts. In Machine Learning, the language of probability and statistics reveals important connections between seemingly disparate algorithms and strategies.Thus, its readers will become articulate in a holistic view of the state-of-the-art and poised to build the next generation of machine learning algorithms.

David BleiPrinceton University
   도서 상세설명   

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.

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