Book Description
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.
Chapter 1: IntroductionChapter 2: Intelligent AgentsChapter 3: Solving Problems by SearchingChapter 4: Search in Complex EnvironmentsChapter 5: Constraint Satisfaction ProblemsChapter 6: Adversarial Search and GamesChapter 7: Logical AgentsChapter 8: First-Order LogicChapter 9: Inference in First-Order LogicChapter 10: Knowledge RepresentationChapter 11: Automated PlanningChapter 12: Quantifying UncertaintyChapter 13: Probabilistic ReasoningChapter 14: Probabilistic Reasoning over TimeChapter 15: Making Simple DecisionsChapter 16: Making Complex DecisionsChapter 17: Multiagent Decision MakingChapter 18: Probabilistic ProgrammingChapter 19: Learning from ExamplesChapter 20: Knowledge in LearningChapter 21: Learning Probabilistic ModelsChapter 22: Deep LearningChapter 23: Reinforcement LearningChapter 24: Natural Language ProcessingChapter 25: Deep Learning for Natural Language ProcessingChapter 26: RoboticsChapter 27: Computer VisionChapter 28: Philosophy, Ethics, and Safety of AIChapter 29: The Future of AI
Artificial Intelligence : A Modern Approach - 4th ed - Stuart Russell - 9781292401133 - Pearson Education
ISBN: 9781292401133
| Publication Year | 2021 |
| Publisher | Pearson Education |
| No. of Pages | 1170 |
| Weight | 2.2 kg |
| Genre | problem solving, constraint satisfaction, knowledge, reasoning, planning |
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.
Chapter 1: IntroductionChapter 2: Intelligent AgentsChapter 3: Solving Problems by SearchingChapter 4: Search in Complex EnvironmentsChapter 5: Constraint Satisfaction ProblemsChapter 6: Adversarial Search and GamesChapter 7: Logical AgentsChapter 8: First-Order LogicChapter 9: Inference in First-Order LogicChapter 10: Knowledge RepresentationChapter 11: Automated PlanningChapter 12: Quantifying UncertaintyChapter 13: Probabilistic ReasoningChapter 14: Probabilistic Reasoning over TimeChapter 15: Making Simple DecisionsChapter 16: Making Complex DecisionsChapter 17: Multiagent Decision MakingChapter 18: Probabilistic ProgrammingChapter 19: Learning from ExamplesChapter 20: Knowledge in LearningChapter 21: Learning Probabilistic ModelsChapter 22: Deep LearningChapter 23: Reinforcement LearningChapter 24: Natural Language ProcessingChapter 25: Deep Learning for Natural Language ProcessingChapter 26: RoboticsChapter 27: Computer VisionChapter 28: Philosophy, Ethics, and Safety of AIChapter 29: The Future of AI
