Efficient learning machines theories, concepts, and applications for engineers and system designers

Description

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 Khannas 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 ... Chapter 1. Machine LearningChapter 2. Machine Learning and Knowledge DiscoveryChapter 3. Support Vector Machines for ClassificationChapter 4. Support Vector RegressionChapter 5. Hidden Markov ModelChapter 6. Bio-Inspired Computing: Swarm IntelligenceChapter 7. Deep Neural NetworksChapter 8. Cortical AlgorithmsChapter 9. Deep LearningChapter 10. Multiobjective OptimizationChapter 11. Machine Learning in Action: Examples