This book is the outgrowth of our teaching advanced undergraduate and graduate
courses over the past 20 years. These courses have been taught to different
audiences, including students in electrical and electronics engineering, computer
engineering, computer science, and informatics, as well as to an interdisciplinary
audience of a graduate course on automation. This experience led us to make
the book as self-contained as possible and to address students with different backgrounds.
As prerequisitive knowledge, the reader require
s only basic calculus,
elementary linear algebra, and some probability theory basics. A number of mathematical
tools, such as probability and statistics as well as constrained optimization,
needed by various chapters, are treated in fourAppendices. The book is designed to
serve as a text for advanced undergraduate and graduate students,and it can be used
for either a one- or a two-semester course. Furthermore,it is intended to be used as a
self-study and reference book for research and for the practicing scientist/engineer.
This latter audience was also our second incentive for writing this book, due to the
involvement of our group in a number of projects related to pattern recognition.