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The first edition of this popular textbook, Contemporary Artificial
Intelligence, provided an accessible and student friendly
introduction to AI. This fully revised and expanded update,
Artificial Intelligence: With an Introduction to Machine Learning,
Second Edition, retains the same accessibility and problem-solving
approach, while providing new material and methods. The book is
divided into five sections that focus on the most useful techniques
that have emerged from AI. The first section of the book covers
logic-based methods, while the second section focuses on
probability-based methods. Emergent intelligence is featured in the
third section and explores evolutionary computation and methods
based on swarm intelligence. The newest section comes next and
provides a detailed overview of neural networks and deep learning.
The final section of the book focuses on natural language
understanding. Suitable for undergraduate and beginning graduate
students, this class-tested textbook provides students and other
readers with key AI methods and algorithms for solving challenging
problems involving systems that behave intelligently in specialized
domains such as medical and software diagnostics, financial
decision making, speech and text recognition, genetic analysis, and
more.
The first edition of this popular textbook, Contemporary Artificial
Intelligence, provided an accessible and student friendly
introduction to AI. This fully revised and expanded update,
Artificial Intelligence: With an Introduction to Machine Learning,
Second Edition, retains the same accessibility and problem-solving
approach, while providing new material and methods. The book is
divided into five sections that focus on the most useful techniques
that have emerged from AI. The first section of the book covers
logic-based methods, while the second section focuses on
probability-based methods. Emergent intelligence is featured in the
third section and explores evolutionary computation and methods
based on swarm intelligence. The newest section comes next and
provides a detailed overview of neural networks and deep learning.
The final section of the book focuses on natural language
understanding. Suitable for undergraduate and beginning graduate
students, this class-tested textbook provides students and other
readers with key AI methods and algorithms for solving challenging
problems involving systems that behave intelligently in specialized
domains such as medical and software diagnostics, financial
decision making, speech and text recognition, genetic analysis, and
more.
This book brings all of the elements of data mining together in a
single volume, saving the reader the time and expense of making
multiple purchases. It consolidates both introductory and advanced
topics, thereby covering the gamut of data mining and machine
learning tactics ? from data integration and pre-processing, to
fundamental algorithms, to optimization techniques and web mining
methodology.
The proposed book expertly combines the finest data mining material
from the Morgan Kaufmann portfolio. Individual chapters are derived
from a select group of MK books authored by the best and brightest
in the field. These chapters are combined into one comprehensive
volume in a way that allows it to be used as a reference work for
those interested in new and developing aspects of data mining.
This book represents a quick and efficient way to unite valuable
content from leading data mining experts, thereby creating a
definitive, one-stop-shopping opportunity for customers to receive
the information they would otherwise need to round up from separate
sources.
* Chapters contributed by various recognized experts in the field
let the reader remain up to date and fully informed from multiple
viewpoints.
* Presents multiple methods of analysis and algorithmic
problem-solving techniques, enhancing the reader's technical
expertise and ability to implement practical solutions.
* Coverage of both theory and practice brings all of the elements
of data mining together in a single volume, saving the reader the
time and expense of making multiple purchases.
Probabilistic Methods for Financial and Marketing Informatics aims
to provide students with insights and a guide explaining how to
apply probabilistic reasoning to business problems. Rather than
dwelling on rigor, algorithms, and proofs of theorems, the authors
concentrate on showing examples and using the software package
Netica to represent and solve problems. The book contains unique
coverage of probabilistic reasoning topics applied to business
problems, including marketing, banking, operations management, and
finance. It shares insights about when and why probabilistic
methods can and cannot be used effectively. This book is
recommended for all R&D professionals and students who are
involved with industrial informatics, that is, applying the
methodologies of computer science and engineering to business or
industry information. This includes computer science and other
professionals in the data management and data mining field whose
interests are business and marketing information in general, and
who want to apply AI and probabilistic methods to their problems in
order to better predict how well a product or service will do in a
particular market, for instance. Typical fields where this
technology is used are in advertising, venture capital decision
making, operational risk measurement in any industry, credit
scoring, and investment science.
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