![ES EXTENSTION TRADING www.1tobuy.com [1TO BUY]](https://cdn.store-assets.com/s/406307/f/7859603.png?width=350)
Artificial Intelligence 4th GE Stuart Russell / Peter Norvig 9781292401133
RM 180.00
TITLE : Artificial Intelligence: A Modern Approach Stuart Russell / Peter Norvig
ISBN13 :9781292401133
PUBLISHER : PEARSON (2021)
EDITION : 4TH GLOBAL EDITION PAPERBACK
PAGES : 1166 PAGES
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.
Table of contents
Part I: Artificial Intelligence
1. Introduction
2. Intelligent Agents
Part II: Problem Solving
3. Solving Problems by Searching
4. Search in Complex Environments
5. Constraint Satisfaction Problems
6. Adversarial Search and Games
Part III: Knowledge and Reasoning
7. Logical Agents
8. First-Order Logic
9. Inference in First-Order Logic
10. Knowledge Representation
11. Automated Planning
12. Quantifying Uncertainty
Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
14. Probabilistic Reasoning over Time
15. Making Simple Decisions
16. Making Complex Decisions
Part V: Learning
17. Multiagent Decision Making
18. Probabilistic Programming
19. Learning from Examples
20. Knowledge in Learning
21. Learning Probabilistic Models
22. Deep Learning
Part VI: Communicating, Perceiving, and Acting
23. Reinforcement Learning
23.7 Applications of Reinforcement Learning
24. Natural Language Processing
25. Deep Learning for Natural Language Processing
26. Robotics
27. Computer Vision
Part VII: Conclusions
28. Philosophy and Ethics of AI
29. The Future of AI
Appendix A:Mathematical Background
A.1 Complexity Analysis and O() Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
Appendix B: Notes on Languages and Algorithms
B.1 Defining Languages with Backus--Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Supplemental Material
1. Introduction
2. Intelligent Agents
Part II: Problem Solving
3. Solving Problems by Searching
4. Search in Complex Environments
5. Constraint Satisfaction Problems
6. Adversarial Search and Games
Part III: Knowledge and Reasoning
7. Logical Agents
8. First-Order Logic
9. Inference in First-Order Logic
10. Knowledge Representation
11. Automated Planning
12. Quantifying Uncertainty
Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
14. Probabilistic Reasoning over Time
15. Making Simple Decisions
16. Making Complex Decisions
Part V: Learning
17. Multiagent Decision Making
18. Probabilistic Programming
19. Learning from Examples
20. Knowledge in Learning
21. Learning Probabilistic Models
22. Deep Learning
Part VI: Communicating, Perceiving, and Acting
23. Reinforcement Learning
23.7 Applications of Reinforcement Learning
24. Natural Language Processing
25. Deep Learning for Natural Language Processing
26. Robotics
27. Computer Vision
Part VII: Conclusions
28. Philosophy and Ethics of AI
29. The Future of AI
Appendix A:Mathematical Background
A.1 Complexity Analysis and O() Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
Appendix B: Notes on Languages and Algorithms
B.1 Defining Languages with Backus--Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Supplemental Material
×
×