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Table of contents

  1. Front Matter
    Pages i-xix
  2. Mariette Awad, Rahul Khanna
    Pages 1-18 Open Access
  3. Mariette Awad, Rahul Khanna
    Pages 19-38 Open Access
  4. Mariette Awad, Rahul Khanna
    Pages 39-66 Open Access
  5. Mariette Awad, Rahul Khanna
    Pages 67-80 Open Access
  6. Mariette Awad, Rahul Khanna
    Pages 81-104 Open Access
  7. Mariette Awad, Rahul Khanna
    Pages 105-125 Open Access
  8. Mariette Awad, Rahul Khanna
    Pages 127-147 Open Access
  9. Mariette Awad, Rahul Khanna
    Pages 149-165 Open Access
  10. Mariette Awad, Rahul Khanna
    Pages 167-184 Open Access
  11. Mariette Awad, Rahul Khanna
    Pages 185-208 Open Access
  12. Mariette Awad, Rahul Khanna
    Pages 209-240 Open Access
  13. Back Matter
    Pages 241-248

About this book

Introduction

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.

Bibliographic information