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Artificial Intelligence: A Modern Approach, eBook, Global Edition, 4. udgave

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Stuart Russell og Peter Norvig
(2021)
Pearson International
399,00 kr.
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Artificial Intelligence: A Modern Approach, eBook, Global Edition, 4. udgave

Artificial Intelligence: A Modern Approach, eBook, Global Edition Vital Source e-bog

Stuart Russell og Peter Norvig
(2021)
Pearson International
345,00 kr.
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Artificial Intelligence: A Modern Approach, eBook, Global Edition, 4. udgave

Artificial Intelligence: A Modern Approach, eBook, Global Edition Vital Source e-bog

Stuart Russell og Peter Norvig
(2021)
Pearson International
284,00 kr.
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Artificial Intelligence: A Modern Approach, Global Edition, 4. udgave
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Stuart Russell og Peter Norvig
(2021)
Pearson International
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Artificial Intelligence: A Modern Approach, Global Edition, 4. udgave
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Stuart Russell og Peter Norvig
(2021)
Pearson International
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Artificial Intelligence: a Modern Approach, Global Edition, 4. udgave

Artificial Intelligence: a Modern Approach, Global Edition

Stuart Russell og Peter Norvig
(2021)
Sprog: Engelsk
Pearson Education, Limited
640,00 kr. 576,00 kr.
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Detaljer om varen

  • 4. Udgave
  • Vital Source 365 day rentals (fixed pages)
  • Udgiver: Pearson International (April 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292401171R365
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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
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Detaljer om varen

  • 4. Udgave
  • Vital Source 180 day rentals (fixed pages)
  • Udgiver: Pearson International (April 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292401171R180
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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Licens varighed:
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
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Detaljer om varen

  • 4. Udgave
  • Vital Source 90 day rentals (fixed pages)
  • Udgiver: Pearson International (April 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292401171R90
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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Licens varighed:
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
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Detaljer om varen

  • 4. Udgave
  • Vital Source searchable e-book (Reflowable pages)
  • Udgiver: Pearson International (December 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292410074
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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: ubegrænset dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 4. Udgave
  • Vital Source searchable e-book (Fixed pages)
  • Udgiver: Pearson International (April 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292401171
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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: ubegrænset dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 4. Udgave
  • Paperback: 1168 sider
  • Udgiver: Pearson Education, Limited (Maj 2021)
  • Forfattere: Stuart Russell og Peter Norvig
  • ISBN: 9781292401133
Explore the ever-expanding, fascinating field of Artificial Intelligence and its latest technologies with this industry-leading text.

Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russel and Peter Norvigis the long-anticipated revision of this market-leading text, exploring the full breadth and depth of the field of Artificial Intelligence (AI).

From robotic planetary explorers to online services with billions of users, the textbook covers a wide range of applications, delving into the advanced methods of reasoning, deep learning, perception and mathematics.

Thoroughly updated and with new content, this latest edition brings you up to date on the latest technological advancements in the field, presenting concepts in a more unified manner.

Some of the changes in the content include:

  • Content that focuses deeper on machine learning rather than the hand-crafted knowledge of engineering.
  • An updated, thorough discussion emphasises deep learning, probabilistic programming, and multi-agent systems.
  • Extensive updates on the Robotics chapter now include content regarding the interaction of robots with humans.
  • A new online site now includes all the exercises for this edition, allowing the team of authors to update and improve them continuously.

Besides studying the methods and technologies, this edition also considers the ethical aspects and values of practicing the discipline. Fairness, integrity, respect, and social good, provide a fundamental framework to the learning process in this edition, studying the impact of AI on society.

With a plethora of topics, exercises, and practical applications, this leading text is the must-read edition of this field, offering a deeper understanding and a multi-faceted approach to this expanding subject.

Contents I Artificial Intelligence Introduction
What Is AI? The Foundations of Artificial Intelligence The History of Artificial Intelligence The State of the Art Risks and Benefits of AI Summary Bibliographical and Historical Notes Intelligent Agents Agents and Environments Good Behavior: The Concept of Rationality The Nature of Environments The Structure of Agents Summary Bibliographical and Historical Notes II Problem Solving Solving Problems by Searching
3.
1 Problem-Solving Agents
3.
2 Example Problems
3.
3 Search Algorithms
3.
4 Uninformed Search Strategies
3.
5 Informed (Heuristic) Search Strategies
3.
6 Heuristic Functions Summary Bibliographical and Historical Notes Search in Complex Environments
4.
1 Local Search and Optimization Problems
4.
2 Local Search in Continuous Spaces
4.
3 Search with Nondeterministic Actions
4.
4 Search in Partially Observable Environments
4.
5 Online Search Agents and Unknown Environments Summary Bibliographical and Historical Notes Constraint Satisfaction Problems
5.
1 Defining Constraint Satisfaction Problems
5.
2 Constraint Propagation: Inference in CSPs
5.
3 Backtracking Search for CSPs
5.
4 Local Search for CSPs
5.
5 The Structure of Problems Summary Bibliographical and Historical Notes Adversarial Search and Games
6.
1 Game Theory
6.
2 Optimal Decisions in Games
6.
3 Heuristic Alpha--Beta Tree Search
6.
4 Monte Carlo Tree Search
6.
5 Stochastic Games
6.
6 Partially Observable Games
6.
7 Limitations of Game Search Algorithms Summary Bibliographical and Historical Notes

Part III: Knowledge and Reasoning Logical Agents
7.
1 Knowledge-Based Agents
7.
2 The Wumpus World
7.
3 Logic
7.
4 Propositional Logic: A Very Simple Logic
7.
5 Propositional Theorem Proving
7.
6 Effective Propositional Model Checking
7.
7 Agents Based on Propositional Logic Summary Bibliographical and Historical Notes First-Order Logic
8.
1 Representation Revisited
8.
2 Syntax and Semantics of First-Order Logic
8.
3 Using First-Order Logic
8.
4 Knowledge Engineering in First-Order Logic Summary Bibliographical and Historical Notes Inference in First-Order Logic
9.
1 Propositional vs. ~First-Order Inference
9.
2 Unification and First-Order Inference
9.
3 Forward Chaining
9.
4 Backward Chaining
9.
5 Resolution Summary Bibliographical and Historical Notes Knowledge Representation
10.
1 Ontological Engineering
10.
2 Categories and Objects
10.
3 Events
10.
4 Mental Objects and Modal Logic
10.
5 for Categories
10.
6 Reasoning with Default Information Summary Bibliographical and Historical Notes Automated Planning
11.
1 Definition of Classical Planning
11.
2 Algorithms for Classical Planning
11.
3 Heuristics for Planning
11.
4 Hierarchical Planning
11.
5 Planning and Acting in Nondeterministic Domains
11.
6 Time, Schedules, and Resources
11.
7 Analysis of Planning Approaches Summary Bibliographical and Historical Notes

Part IV: Uncertain Knowledge and Reasoning Quantifying Uncertainty
12.
1 Acting under Uncertainty
12.
2 Basic Probability Notation
12.
3 Inference Using Full Joint Distributions
12.
4 Independence
12.
5 Bayes'' Rule and Its Use
12.
6 Naive Bayes Models
12.
7 The Wumpus World Revisited Summary Bibliographical and Historical Notes Probabilistic Reasoning
13.
1 Representing Knowledge in an Uncertain Domain
13.
2 The Semantics of Bayesian Networks
13.
3 Exact Inference in Bayesian Networks
13.
4 Approximate Inference for Bayesian Networks
13.
5 Causal Networks Summary Bibliographical and Historical Notes Probabilistic Reasoning over Time
14.
1 Time and Uncertainty
14.
2 Inference in Temporal Models
14.
3 Hidden Markov Models
14.
4 Kalman Filters
14.
5 Dynamic Bayesian Networks Summary Bibliographical and Historical Notes Probabilistic Programming
15.
1 Relational Probability Models
15.
2 Open-Universe Probability Models
15.
3 Keeping Track of a Complex World
15.
4 Programs as Probability Models Summary Bibliographical and Historical Notes Making Simple Decisions
16.
1 Combining Beliefs and Desires under Uncertainty
16.
2 The Basis of Utility Theory
16.
3 Utility Functions
16.
4 Multiattribute Utility Functions
16.
5 Decision Networks
16.
6 The Value of Information
16.
7 Unknown Preferences Summary Bibliographical and Historical Notes Making Complex Decisions
17.
1 Sequential Decision Problems
17.
2 Algorithms for MDPs 173 Bandit Problems
17.
4 Partially Observable MDPs
17.
5 Algorithms for Solving POMDPs Summary Bibliographical and Historical Notes Multiagent Decision Making
18.
1 Properties of Multiagent Environments
18.
2 Non-Cooperative Game Theory
18.
3 Cooperative Game Theory
18.
4 Making Collective Decisions Summary Bibliographical and Historical Notes V Machine Learning Learning from Examples
19.
1 Forms of Leaming
19.
2 Supervised Learning
19.
3 Learning Decision Trees
19.
4 Model Selection and Optimization
19.
5 The Theory of Learning
19.
6 Linear Regression and Classification
19.
7 Nonparametric Models
19.
8 Ensemble Learning
19.
9 Developing Machine Learning Systen Summary Bibliographical and Historical Notes Learning Probabilistic Models
20.
1 Statistical Learning
20.
2 Learning with Complete Data
20.
3 Learning with Hidden Variables: The EM Algorithm Summary Bibliographical and Historical Notes Deep Learning
21.
1 Simple Feedforward Networks
21.
2 Computation Graphs for Deep Learning
21.
3 Convolutional Networks
21.
4 Learning Algorithms
21.
5 Generalization
21.
6 Recurrent Neural Networks
21.
7 Unsupervised Learning and Transfer Learning
21.
8 Applications Summary Bibliographical and Historical Notes Reinforcement Learning
22.
1 Learning from Rewards
22.
2 Passive Reinforcement Learning
22.
3 Active Reinforcement Learning
22.
4 Generalization in Reinforcement Learning
22.
5 Policy Search
22.
6 Apprenticeship and Inverse Reinforcement Leaming
22.
7 Applications of Reinforcement Learning Summary Bibliographical and Historical Notes VI Communicating, perceiving, and acting Natural Language Processing
23.
1 Language Models
23.
2 Grammar
23.
3 Parsing
23.
4 Augmented Grammars
23.
5 Complications of Real Natural Languagr
23.
6 Natural Language Tasks Summary Bibliographical and Historical Notes Deep Learning for Natural Language Processing
24.
1 Word Embeddings
24.
2 Recurrent Neural Networks for NLP
24.
3 Sequence-to-Sequence Models
24.
4 The Transformer Architecture
24.
5 Pretraining and Transfer Learning
24.
6 State of the art Summary Bibliographical and Historical Notes Computer Vision
25.
1 Introduction
25.
2 Image Formation
25.
3 Simple Image Features
25.
4 Classifying Images
25.
5 Detecting Objects
25.
6 The 3D World
25.
7 Using Computer Vision Summary Bibliographical and Historical Notes Robotics
26.
1 Robots
26.
2 Robot Hardware
26.
3 What kind of problem is robotics solving?
26.
4 Robotic Perception
26.
5 Planning and Control
26.
6 Planning Uncertain Movements
26.
7 Reinforcement Lamin
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