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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.

Table of Contents

2015 | OriginalPaper | Chapter Open Access

Chapter 1. Machine Learning

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 2. Machine Learning and Knowledge Discovery

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 3. Support Vector Machines for Classification

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 4. Support Vector Regression

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 5. Hidden Markov Model

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 6. Bioinspired Computing: Swarm Intelligence

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 7. Deep Neural Networks

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 8. Cortical Algorithms

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 9. Deep Learning

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 10. Multiobjective Optimization

Mariette Awad, Rahul Khanna

2015 | OriginalPaper | Chapter Open Access

Chapter 11. Machine Learning in Action: Examples

Mariette Awad, Rahul Khanna
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