Artificial intelligence : a modern approach
Material type:
TextPublication details: Noida Pearson 2022Edition: 4Description: 1288 pISBN: 9789356063570Subject(s): artificial intelligenceDDC classification: 006.3 Summary: The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Features
Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details.
A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
NEW - New chapters feature expanded coverage of probabilistic programming; multiagent decision making; deep learning; and deep learning for natural language processing.
| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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Kannur University Central Library Stack | Stack | 006.3 RUS/A (Browse shelf (Opens below)) | Available | 59769 |
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| 006.3 RAJ/N Neural Networks,Fuzzy Logic and Genetic Algorithms | 006.3 RIC/A Artificial intelligence | 006.3 RIC/A Artificial intelligence | 006.3 RUS/A Artificial intelligence : a modern approach | 006.3 SIV/P Principles of soft computing | 006.3 SIV/P Principles of soft computing | 006.3 THU/D Data mining: technologies, techniques, tools and trends |
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Features
Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details.
A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
NEW - New chapters feature expanded coverage of probabilistic programming; multiagent decision making; deep learning; and deep learning for natural language processing.
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