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Machine Learning

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Machine Learning, 9780070428072 (0070428077), McGraw-Hill, 1997
The field of machine learning is concerned with the question of how to construct
computer programs that automatically improve with experience. In recent years
many successful machine learning applications have been developed, ranging from
data-mining programs that learn to detect fraudulent credit card transactions, to
information-filtering systems that learn users' reading preferences, to autonomous
vehicles that learn to drive on public highways. At the same time, there have been
important advances in the theory and algorithms that form the foundations of this
field.

The goal of this textbook is to present the key algorithms and theory that
form the core of machine learning. Machine learning draws on concepts and
results from many fields, including statistics, artificial intelligence, philosophy,
information theory, biology, cognitive science, computational complexity, and
control theory. My belief is that the best way to learn about machine learning is
to view it from all of these perspectives and to understand the problem settings,
algorithms, and assumptions that underlie each. In the past, this has been difficult
due to the absence of a broad-based single source introduction to the field. The
primary goal of this book is to provide such an introduction.

Because of the interdisciplinary nature of the material, this book makes
few assumptions about the background of the reader. Instead, it introduces basic
concepts from statistics, artificial intelligence, information theory, and other disciplines
as the need arises, focusing on just those concepts most relevant to machine
learning. The book is intended for both undergraduate and graduate students in
fields such as computer science, engineering, statistics, and the social sciences,
and as a reference for software professionals and practitioners. Two principles
that guided the writing of the book were that it should be accessible to undergraduate
students and that it should contain the material I would want my own Ph.D.
students to learn before beginning their doctoral research in machine learning.
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