machine learning a probabilistic perspective

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Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262018029
Genre : Computers
File Size : 87. 95 MB
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262304320
Genre : Computers
File Size : 54. 66 MB
Format : PDF, ePub
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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.

Study Guide For Machine Learning

Author : Cram101 Publishing
ISBN : 1490227636
Genre : Machine learning
File Size : 65. 56 MB
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Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780262018029 .

Bayesian Reasoning And Machine Learning

Author : David Barber
ISBN : 9780521518147
Genre : Computers
File Size : 84. 8 MB
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Machine Learning A Probabilistic Perspective

Author : CTI Reviews
ISBN : 9781490269269
Genre : Education
File Size : 23. 23 MB
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Facts101 is your complete guide to Machine Learning, A Probabilistic Perspective. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

Machine Learning

Author : Stephen Marsland
ISBN : 1420067192
Genre : Computers
File Size : 30. 72 MB
Format : PDF
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Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

Probabilistic Graphical Models

Author : Daphne Koller
ISBN : 9780262013192
Genre : Computers
File Size : 60. 97 MB
Format : PDF, ePub
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A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

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