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Artificial intelligence : A modern approach / Stuart J. Russell and Peter Norvig

By: Contributor(s): Material type: TextTextPublication details: Noida, India : Pearson India Education Services, c2015Edition: THIRD EDITIONDescription: xviii, 1145 pages : illustrations ; 28 cmISBN:
  • 9789332543515
Subject(s): LOC classification:
  • Q 335 .S78 2015
Contents:
I. Artificial intelligence -- II. Problem-solving -- III. Knowledge -- IV. Uncertain knowledge and reasoning -- V. Learning -- VI. Communicating, perceiving, and acting -- VII. Conclusion.
Summary: In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises.
Item type: Books
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Graduate Studies General Circulation Gen. Ed. - CCIT GC Q 335 .S78 2015 (Browse shelf(Opens below)) c.1 Available NULIB000014041

Includes bibliographical references and index.

I. Artificial intelligence -- II. Problem-solving -- III. Knowledge -- IV. Uncertain knowledge and reasoning -- V. Learning -- VI. Communicating, perceiving, and acting -- VII. Conclusion.

In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises.

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