The purpose of machine learning is to program computers to use sample data or past experience to solve a given problem. There are now several successful applications of machine learning, including systems that analyze past sales data to predict customer behavior, recognize faces or speech, and optimize robot behavior so that a task can be resourced. Minimize and extract knowledge from bioinformatics data.
"Machine Learning" is a comprehensive book on the subject, which covers a wide range of topics that are not usually mentioned in textbooks. Different methods based on different areas including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control and data mining so that discuss the same approach to problems and machine learning solutions.
All learning algorithms are explained in such a way that the student can move directly from the book equations to computer programs. This book can be used by advanced undergraduate and graduate students who have taken courses in computer programming, probability, differential calculus, integrals and linear algebra.
This book will also be useful for engineers interested in applying machine learning techniques. Following an introduction that defines machine learning and examples of applications of machine learning, the book covers concept learning, theoretical learning, decision tree learning, neural networks, business decision theory, parametric methods, and multi-parameter methods.