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Mastering Java Machine Learning

You're reading from Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785880513

Length 556 pages

Edition 1st Edition

Languages

Java

Concepts

Big Data

Authors (2):

Uday Kamath

Krishna Choppella

View More author details

Table of Contents (20) Chapters

Mastering Java Machine Learning

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Machine Learning Review

2. Practical Approach to Real-World Supervised Learning FREE CHAPTER

3. Unsupervised Machine Learning Techniques

4. Semi-Supervised and Active Learning

5. Real-Time Stream Machine Learning

6. Probabilistic Graph Modeling

7. Deep Learning

8. Text Mining and Natural Language Processing

9. Big Data Machine Learning – The Final Frontier

Linear Algebra

Probability

Index

References

Daphne Koller and Nir Friedman (2009). Probabilistic Graphical Models. MIT Press. ISBN 0-262-01319-3.
T. Verma and J. Pearl (1988), In proceedings for fourth workshop on Uncertainty in Artificial Intelligence, Montana, Pages 352-359. Causal Networks- Semantics and expressiveness.
Dagum, P., and Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP hard. Artificial Intelligence 60(1):141–153.
U. Bertele and F. Brioschi, Nonserial Dynamic Programming, Academic Press. New York, 1972.
Shenoy, P. P. and G. Shafer (1990). Axioms for probability and belief-function propagation, in Uncertainty in Artificial Intelligence, 4, 169-198, North-Holland, Amsterdam
Bayarri, M.J. and DeGroot, M.H. (1989). Information in Selection Models. Probability and Bayesian Statistics, (R. Viertl, ed.), Plenum Press, New York.
Spiegelhalter and Lauritzen (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks 20. Pages 579-605.
David...

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Authors (2)

Kamath

Kamath

See other products by Kamath

Krishna Choppella

Krishna Choppella

Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.

See other products by Krishna Choppella

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