There is no doubt that Artificial Intelligence (AI) is an immense domain and equally no arguments against the fact that this is an extensive work. The authors have attempted to map and present the entire spectrum of the field which includes but is not limited to – logic and probabilistic theories, perception how-to, reasoning, learning and action performance , as well as from the realm of microelectronics to a planetary exploration robotic device. Further, the book is also extensive since we do dig in a little.
One of the subtitles of the book that would conjugate its content into a single word would be “A Modern Approach”. What the authors would mean by this rather vague phrase, the authors wish to draw the picture as it is and not have to try to provide a history of all the angles of AI.
As you know, the technology is enhancing day by day and people are growing with very high speed. Nowadays, AI has been entered in the technology.
This is the big advancement in growing field for all of us. And an artificial intelligence is one of the best field about which you must have enough knowledge to be successful in near future.
Authors of this book:
Stuart Russell: There are 100 pages in artificial intelligence written by Stuart Russell.
Peter Norvig: He was a professor at the University of Southern California. He was also a research faculty member at Berkeley.
Book Details:
Category: Artificial Intelligence
Edition: 3rd
Format: B/W
Language: English
Pages: 1151
Type : Technology Textbook / Reference
Price
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Contents:
I Artificial Intelligence
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Adversarial Search and Games
6 Constraint Satisfaction Problems
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Probabilistic Programming
16 Making Simple Decisions
17 Making Complex Decisions
18 Multi-agent Decision Making
V Machine Learning
19 Learning from Examples
20 Learning Probabilistic Models
21 Deep Learning
22 Reinforcement Learning
VI Communicating, perceiving, and acting
23 Natural Language Processing
24 Deep Learning for Natural Language Processing
25 Computer Vision
26 Robotics
VII Conclusions
27 Philosophy, Ethics, and Safety of AI
28 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
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