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  • Book
  • Open Access
  • © 2015

Efficient Learning Machines

Theories, Concepts, and Applications for Engineers and System Designers

Apress

Authors:

  • Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.

Buying options

Softcover Book
USD 39.99
Price excludes VAT (USA)

Table of contents (11 chapters)

  1. Front Matter

    Pages i-xix
  2. Machine Learning

    • Mariette Awad, Rahul Khanna
    Pages 1-18Open Access
  3. Machine Learning and Knowledge Discovery

    • Mariette Awad, Rahul Khanna
    Pages 19-38Open Access
  4. Support Vector Machines for Classification

    • Mariette Awad, Rahul Khanna
    Pages 39-66Open Access
  5. Support Vector Regression

    • Mariette Awad, Rahul Khanna
    Pages 67-80Open Access
  6. Hidden Markov Model

    • Mariette Awad, Rahul Khanna
    Pages 81-104Open Access
  7. Bioinspired Computing: Swarm Intelligence

    • Mariette Awad, Rahul Khanna
    Pages 105-125Open Access
  8. Deep Neural Networks

    • Mariette Awad, Rahul Khanna
    Pages 127-147Open Access
  9. Cortical Algorithms

    • Mariette Awad, Rahul Khanna
    Pages 149-165Open Access
  10. Deep Learning

    • Mariette Awad, Rahul Khanna
    Pages 167-184Open Access
  11. Multiobjective Optimization

    • Mariette Awad, Rahul Khanna
    Pages 185-208Open Access
  12. Machine Learning in Action: Examples

    • Mariette Awad, Rahul Khanna
    Pages 209-240Open Access
  13. Back Matter

    Pages 241-248

About this book

Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.

Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.

Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms.

Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

About the authors

Rahul Khanna is a platform architect at Intel Corporation involved in development of energy-efficient algorithms. Over the past 17 years he has worked on server system software technologies, including platform automation, power/thermal optimization techniques, reliability, optimization, and predictive methodologies. He has authored numerous technical papers and book chapters in the areas related to energy optimization, platform wireless interconnects, sensor networks, interconnect reliability, predictive modeling, motion estimation, and security. He holds 27 patents. He is the co-inventor of the Intel IBIST methodology for High-Speed interconnect testing. His research interests include machine learning-based power/thermal optimization algorithms, narrow-channel high-speed wireless interconnects, and information retrieval in dense sensor networks. Rahul is member of IEEE and the recipient of three Intel Achievement Awards for his contributions in areas related to advancements of platform technologies. He is the author of A Vision for Platform Autonomy: Robust Frameworks for Systems.

Bibliographic Information

  • Book Title: Efficient Learning Machines

  • Book Subtitle: Theories, Concepts, and Applications for Engineers and System Designers

  • Authors: Mariette Awad, Rahul Khanna

  • DOI: https://doi.org/10.1007/978-1-4302-5990-9

  • Publisher: Apress Berkeley, CA

  • eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2015

  • License: CC BY

  • Softcover ISBN: 978-1-4302-5989-3

  • eBook ISBN: 978-1-4302-5990-9

  • Edition Number: 1

  • Number of Pages: XIX, 268

  • Number of Illustrations: 88 b/w illustrations

  • Topics: Artificial Intelligence

Buying options

Softcover Book
USD 39.99
Price excludes VAT (USA)