Learning through personal experience and knowledge, which propagates from generation to generation, is at the heart of human intelligence. Also, at the heart of any scientific field lies the development of models (often, they are called theories) in order to explain the available experimental evidence at each time period. In other words, we always learn from data. Different data and different focuses on the data give rise to different scientific disciplines.

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This book is about learning from data; in particular, our intent is to detect and unveil a possible hidden structure and regularity patterns associated with their generation mechanism. This information in turn helps our analysis and understanding of the nature of the data, which can be used to make predictions for the future. Besides modeling the underlying structure, a major direction of significant interest in Machine Learning is to develop efficient algorithms for designing the models and also for analysis and prediction.

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The latter part is gaining importance in the dawn of what we call the big data era, when one has to deal with massive amounts of data, which may be represented in spaces of very large dimensionality. Analyzing data for such applications sets demands on algorithms to be computationally efficient and at the same time robust in their performance, because some of these data are contaminated with large noise and also, in some cases, the data may have missing values.

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Such methods and techniques have been at the center of scientific research for a number of decades in various disciplines, such as Statistics and Statistical Learning, Pattern Recognition, Signal and Image Processing and Analysis, Computer Science, Data Mining, Machine Vision, Bioinformatics, Industrial Automation, and Computer-Aided Medical Diagnosis, to name a few. In spite of the different names, there is a common corpus of techniques that are used in all of them, and we will refer to such methods as Machine Learning.

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