Machine Learning is a name that is gaining popularity as an umbrella formethods that have been studied
and developed for many decades in different scientific communities and under different names, such as
Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing,
Image Processing and Analysis, System Identification and Control, Data Mining and Information
Retrieval, Computer Vision, and Computational Learning
The name “Machine Learning” indicates
what all these disciplines have in common, that is, to learn from data, and then make predictions.
What one tries to learn from data is their underlying structure and regularities, via the development of
a model, which can then be used to provide predictions.
To this end, a number of diverse approaches have been developed, ranging from optimization of cost
functions, whose goal is to optimize the deviation between what one observes from data and what the
model predicts, to probabilistic models that attempt to model the statistical properties of the observed
The goal of this book is to approach the machine learning discipline in a unifying context,
by presenting the major paths and approaches that have been followed over the years, without giving
preference to a specific one.
It is the author’s belief that all of them are valuable to the newcomer who
wants to learn the secrets of this topic, from the applications as well as from the pedagogic point of view.
As the title of the book indicates, the emphasis is on the processing and analysis front of machine
learning and not on topics concerning the theory of learning itself and related performance bounds