2021 Artificial lowest Intelligence: A Modern Approach (Pearson popular Series in Artifical Intelligence) sale

2021 Artificial lowest Intelligence: A Modern Approach (Pearson popular Series in Artifical Intelligence) sale

2021 Artificial lowest Intelligence: A Modern Approach (Pearson popular Series in Artifical Intelligence) sale
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The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
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.

About the Author

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.


Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.


The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.

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4.6 out of 54.6 out of 5
262 global ratings

Top reviews from the United States

Thomas J Ballatore
5.0 out of 5 starsVerified Purchase
Everything I was hoping for!
Reviewed in the United States on May 25, 2020
While I loved the 3rd edition of this book, I''ve been looking forward for years for this update to *the* leading AI textbook. I was giddy when unboxing this and so far it hasn''t disappointed. Clarifications. (1) It''s... See more
While I loved the 3rd edition of this book, I''ve been looking forward for years for this update to *the* leading AI textbook. I was giddy when unboxing this and so far it hasn''t disappointed.

Clarifications. (1) It''s actually a hardcover. The amazon description says "Paperback: 1136 pages" but, as expected, it''s a well-bound hardcover. (2) Another reviewer noted that the print is very light. In my copy, the print is just fine but I did notice that the pages are quite thin. They seem sturdy but are relatively transparent...I assume this is a simple tradeoff—they want to pack the 1100+ pages into a reasonable size and weight. Overall, it feels great to hold and flip through.

Observations. (1) It''s in color! I wasn''t expecting that but it makes sense given the addition of many more images, esp. on computer vision. (2) I skipped straight to Chapter 21 on Deep Learning, authored by Ian Goodfellow. Overall, they must have a great editor because the tone and style match well the "Russell/Norvig style" you might be used to from previous editions. I assume the other guest-written chapters are the same. (3) It''s not cheap but I''d expect it to hold value for many years as it''s unlikely to be updated for ~10 years and there will be significant demand for used copies if you decided to resell. (4) Minor point but I like the smell—it''s definitely a real book!

Overall, if you are interested in AI and want the most comprehensive overview, buy now!

P.S. Well done adding Ada Lovelace to the already though-provoking cover.
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calvinnme
4.0 out of 5 stars
Don''t you hate it when such an expensive book doesn''t have a table of contents listed?
Reviewed in the United States on December 16, 2020
So here it is: Part I: Artificial Intelligence 1. Introduction 1.1 What Is AI? 1.2 The Foundations of Artificial Intelligence 1.3 The History of Artificial Intelligence 1.4 The State of the Art 1.5 Risks and Benefits of... See more
So here it is:
Part I: Artificial Intelligence
1. Introduction
1.1 What Is AI?
1.2 The Foundations of Artificial Intelligence
1.3 The History of Artificial Intelligence
1.4 The State of the Art
1.5 Risks and Benefits of AI
2. Intelligent Agents
2.1 Agents and Environments
2.2 Good Behavior: The Concept of Rationality
2.3 The Nature of Environments
2.4 The Structure of Agents

Part II: Problem Solving
3. 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
4. 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
5. Adversarial Search and Games
5.1 Game Theory
5.2 Optimal Decisions in Games
5.3 Heuristic Alpha--Beta Tree Search
5.4 Monte Carlo Tree Search
5.5 Stochastic Games
5.6 Partially Observable Games
5.7 Limitations of Game Search Algorithms
6. Constraint Satisfaction Problems
6.1 Defining Constraint Satisfaction Problems
6.2 Constraint Propagation: Inference in CSPs
6.3 Backtracking Search for CSPs
6.4 Local Search for CSPs
6.5 The Structure of Problems

Part III: Knowledge and Reasoning
7. 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
8. 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
9. 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
10. Knowledge Representation
10.1 Ontological Engineering
10.2 Categories and Objects
10.3 Events
10.4 Mental Objects and Modal Logic
10.5 Reasoning Systems for Categories
10.6 Reasoning with Default Information
11. 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
12. 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

Part IV: Uncertain Knowledge and Reasoning
13. 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
14. 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
15. 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
16. 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
17. Making Complex Decisions
17.1 Sequential Decision Problems
17.2 Algorithms for MDPs
17.3 Bandit Problems
17.4 Partially Observable MDPs
17.5 Algorithms for solving POMDPs

Part V: Learning
18. 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
19. Learning from Examples
19.1 Forms of Learning
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 Systems
20. Learning Probabilistic Models
20.1 Statistical Learning
20.2 Learning with Complete Data
20.3 Learning with Hidden Variables: The EM Algorithm
21. Deep Learning
21.1 Simple Feedforward Networks
21.2 Mixing and matching models, loss functions and optimizers
21.3 Loss functions
21.4 Models
21.5 Optimization Algorithms
21.6 Generalization
21.7 Recurrent neural networks
21.8 Unsupervised, semi-supervised and transfer learning
21.9 Applications

Part VI: Communicating, Perceiving, and Acting
22. Reinforcement Learning
22.1 Learning from Rewards
22.2 Passive Reinforcement Learning
22.3 Active Reinforcement Learning
22.4 Safe Exploration
22.5 Generalization in Reinforcement Learning
22.6 Policy Search
22.7 Applications of Reinforcement Learning
23. Natural Language Processing
23.1 Language Models
23.2 Grammar
23.3 Parsing
23.4 Augmented Grammars
23.5 Complications of Real Natural Language
23.6 Natural Language Tasks
24. Deep Learning for Natural Language Processing
24.1 Limitations of Feature-Based NLP Models
24.2 Word Embeddings
24.3 Recurrent Neural Networks
24.4 Sequence-to-sequence Models
24.5 The Transformer Architecture
24.6 Pretraining and Transfer Learning
24.7 Introduction
24.8 Image Formation
24.9 Simple Image Features
24.10 Classifying Images
24.11 Detecting Objects
24.12 The 3D World
24.13 Using Computer Vision
25. Robotics
25.1 Robots
25.2 Robot Hardware
25.3 What kind of problem is robotics solving?
25.4 Robotic Perception
25.5 Planning and Control
25.6 Planning Uncertain Movements
25.7 Reinforcement Learning in Robotics
25.8 Humans and Robots
25.9 Alternative Robotic Frameworks
25.10 Application Domains

Part VII: Conclusions
26. Philosophy and Ethics of AI
26.1 Weak AI: What are the Limits of AI?
26.2 Strong AI: Can Machines Really Think?
26.3 The Ethics of AI
27. The Future of AI
27.1 AI Components
27.2 AI Architectures

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
54 people found this helpful
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Eli T
1.0 out of 5 starsVerified Purchase
Kindle Version of 4th Edition is Horribly Formatted
Reviewed in the United States on May 21, 2021
The kindle version of the 4th edition (ISBN 978-0134610993) is nearly unreadable. The publisher did not include a table-of-contents for navigating within the Kindle App, and most of the math equations, along with the sentences that contain them, are chopped and jumbled into... See more
The kindle version of the 4th edition (ISBN 978-0134610993) is nearly unreadable. The publisher did not include a table-of-contents for navigating within the Kindle App, and most of the math equations, along with the sentences that contain them, are chopped and jumbled into an unintelligible mess.
20 people found this helpful
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MJ
1.0 out of 5 starsVerified Purchase
Kindle Edition - Format is Completely Messed Up! Don''t buy the Kindle Edition!
Reviewed in the United States on May 24, 2021
Don''t buy the Kindle Edition. The format is completely messed up. The formulas are not displaying correctly and it is impossible to figure them out. I an including 3 samples screenshots of the terrible way the formulas are being displayed. These are only 3 of many. The... See more
Don''t buy the Kindle Edition. The format is completely messed up. The formulas are not displaying correctly and it is impossible to figure them out. I an including 3 samples screenshots of the terrible way the formulas are being displayed. These are only 3 of many.
The index is incomplete, it only shows part of the items for the letter A, and then there are no more pages in the book!
I''m going to try to get a refund.
19 people found this helpful
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John Pearson
1.0 out of 5 starsVerified Purchase
Avoid - book does not render correctly
Reviewed in the United States on June 3, 2021
I have tried the kindle app on my iphone, my ipad and my macbook and in all cases it renders poorly. Whenever the content talks about two objects and labels them as "A" and "B", the render moves the A and B characters to random places. The problem is shown in the screenshot... See more
I have tried the kindle app on my iphone, my ipad and my macbook and in all cases it renders poorly. Whenever the content talks about two objects and labels them as "A" and "B", the render moves the A and B characters to random places. The problem is shown in the screenshot where the A and B have moved to the start and end of the sentence. This is happening about every second page.

The content so far seems good but it''s just too much of a struggle trying to continually get the sense of sentences and then work out where the misplaced characters belong.

Amazon - if you are watching, you can mail me a hard copy at your expense.
6 people found this helpful
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ZZ
3.0 out of 5 starsVerified Purchase
Poor print quality
Reviewed in the United States on August 16, 2020
The quality of the fourth edition is pretty poor: pages are thin, binding is weak, smudges.
10 people found this helpful
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Adam Zhang
3.0 out of 5 starsVerified Purchase
Bookbinding issue
Reviewed in the United States on November 1, 2020
I know it''s a fantastic book. I''ve read the previous version for school. I''m buying this one just because I want to collect it, and the very first page has a bookbinding issue. Requesting an exchange, hopeful that the new one won''t be the same.
8 people found this helpful
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Reader10928
4.0 out of 5 starsVerified Purchase
Great, but missing details that make it challenging for beginners
Reviewed in the United States on November 20, 2020
I like that this book covers a lot of ground and provides super useful information and algorithms. Super great book. I don''t like that some parts in the algorithm pseudo code aren''t thoroughly explained with examples of usage. Also, the formulae notation for the... See more
I like that this book covers a lot of ground and provides super useful information and algorithms. Super great book.

I don''t like that some parts in the algorithm pseudo code aren''t thoroughly explained with examples of usage. Also, the formulae notation for the i''th and j''th iteration characters aren''t properly explained or consistent across formulae, making it more difficult for beginners. I think the formulae notations could be more consistent and thoroughly explained.
4 people found this helpful
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Top reviews from other countries

Ben
5.0 out of 5 starsVerified Purchase
Worth getting the 4th Edition
Reviewed in the United Kingdom on August 17, 2020
I ordered a copy to the UK from Amazon US. Presumably, an international edition will become available but I can''t find one at time of writing (Aug 2020). The copy I received appears genuine but arrived with a printing defect on the title page, which was irritating given it...See more
I ordered a copy to the UK from Amazon US. Presumably, an international edition will become available but I can''t find one at time of writing (Aug 2020). The copy I received appears genuine but arrived with a printing defect on the title page, which was irritating given it cost £135. The pages are not bad quality but are thin enough you can see the text on the other side pretty clearly. I guess this is reasonable given the book has a little over a thousand pages. It is printed in full-colour. Right now the 4th edition (2020) is significantly more expensive than the 3rd. I recommend spending the extra money. The 3rd edition is from 2010 and the 4th isn''t just minor changes - To quote the book: "Overall, about 25% of the material in the book is brand new. The remaining 75% has been largely re-written to present a more unified picture of the field. 22% of the citations in this edition are to works published after 2010." As the book is a classic and seems to be the most commonly recommended textbook for this field I won''t spend long trying to sell you on the content - it''s excellent as expected. So far my impression is it''s extremely well written, well-edited and vast in scope. A criticism I saw levelled at the third edition was that it was difficult to use as a reference due to a lack of detail in the Contents and badly executed Index. This appears to have been corrected for the 4th ed. The Contents explains each chapter to a good level of detail. The Index and Bibliography appear comprehensive. I''m very happy with the purchase and sure it will be a valuable reference throughout studying a Machine Learning/AI related masters.
I ordered a copy to the UK from Amazon US. Presumably, an international edition will become available but I can''t find one at time of writing (Aug 2020).

The copy I received appears genuine but arrived with a printing defect on the title page, which was irritating given it cost £135. The pages are not bad quality but are thin enough you can see the text on the other side pretty clearly. I guess this is reasonable given the book has a little over a thousand pages. It is printed in full-colour.

Right now the 4th edition (2020) is significantly more expensive than the 3rd. I recommend spending the extra money. The 3rd edition is from 2010 and the 4th isn''t just minor changes - To quote the book:

"Overall, about 25% of the material in the book is brand new. The remaining 75% has been largely re-written to present a more unified picture of the field. 22% of the citations in this edition are to works published after 2010."

As the book is a classic and seems to be the most commonly recommended textbook for this field I won''t spend long trying to sell you on the content - it''s excellent as expected. So far my impression is it''s extremely well written, well-edited and vast in scope.

A criticism I saw levelled at the third edition was that it was difficult to use as a reference due to a lack of detail in the Contents and badly executed Index. This appears to have been corrected for the 4th ed. The Contents explains each chapter to a good level of detail. The Index and Bibliography appear comprehensive.

I''m very happy with the purchase and sure it will be a valuable reference throughout studying a Machine Learning/AI related masters.
8 people found this helpful
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Ben Fraser
5.0 out of 5 starsVerified Purchase
The definitive and most comprehensive book on Artificial Intelligence.
Reviewed in the United Kingdom on May 22, 2021
For anyone who has studied the previous version(s) of this book, you''ll know just how detailed and incredibly comprehensive this is. It is generally agreed to be the most credible and thorough book within the field of AI, by virtue of the backgrounds of the primary authors,...See more
For anyone who has studied the previous version(s) of this book, you''ll know just how detailed and incredibly comprehensive this is. It is generally agreed to be the most credible and thorough book within the field of AI, by virtue of the backgrounds of the primary authors, along with its vast size of 1100+ pages. Even at this vast length, the information distilled into each of the sections is dense and very valuable, and I cant really highlight any chapters that are particularly redundant. You can also gain an incredible amount of knowledge from each of the sections of the book, even without a full understanding of the concepts the first time round (or reading many of the earlier chapters). For this reason it is widely used as a primary reference throughout academic courses in AI. Where this book really shines is its heavy focus on foundational AI principles and topics that are concrete and timeless (as timeless as concepts can be in such a fast-moving field!). The modernised field of AI is heavily dominated by machine learning, but this represents only a subset of the field, with a vast expanse of other important subfields. This book branches across into all of these other areas (along with strong coverage of machine learning too), and if studied, will provide you with a very strong and grounded foundation in AI. It should be highlighted that the book is challenging, and is far from a simple read. It is very good as a reference book, and for dipping into and out of as required - it''s unlikely you''ll manage to commit to reading the book from start to finish. This is not due to fault of the authors, who do a fantastic job of using engaging and easily-read writing styles, but is simply by virtue of the complicated and vast topics that the book is based on. In terms of the 4th Edition itself, I would fully recommend this updated version over any of the previous versions. There is a significant amount of new material and improvements compared to the 3rd edition, which helps capture the major developments throughout the past ten years. There are now extensive chapters on Deep learning, probabilistic programming, multi-agent architectures, natural language processing, computer vision and robotics. Furthermore, the book now has a much better glossary with huge range of topics to quickly find, which was definitely a downside of prior editions. In terms of the physical book itself, I have no major issues with the 4th Edition and can summarise as follows: - It is huge (1100+ pages). - It is expensive, but nevertheless a very good investment. It is unlikely another edition of this book will be released for a long time (last version was published in 2010), and so this will stand the test of time for now. - High quality hardback, with good binding (contrary to other reviews I have read). - The pages are thin, but nevertheless high quality, with great use of colour on all pages, illustrations and diagrams. - The book shipped from the US to the UK for delivery, and despite this still arrived pristine without any damage at all. I ordered through Amazon with Book Depository, who packaged it very carefully in a decent sized box with bubble wrap. I know some other sellers might not be so diligent, which isn''t worth taking the chance with such an expensive book. Overall, I thoroughly recommend. An essential book for the collection of any AI researchers, students, data scientists or AI practitioners.
For anyone who has studied the previous version(s) of this book, you''ll know just how detailed and incredibly comprehensive this is. It is generally agreed to be the most credible and thorough book within the field of AI, by virtue of the backgrounds of the primary authors, along with its vast size of 1100+ pages. Even at this vast length, the information distilled into each of the sections is dense and very valuable, and I cant really highlight any chapters that are particularly redundant. You can also gain an incredible amount of knowledge from each of the sections of the book, even without a full understanding of the concepts the first time round (or reading many of the earlier chapters). For this reason it is widely used as a primary reference throughout academic courses in AI.

Where this book really shines is its heavy focus on foundational AI principles and topics that are concrete and timeless (as timeless as concepts can be in such a fast-moving field!). The modernised field of AI is heavily dominated by machine learning, but this represents only a subset of the field, with a vast expanse of other important subfields. This book branches across into all of these other areas (along with strong coverage of machine learning too), and if studied, will provide you with a very strong and grounded foundation in AI.

It should be highlighted that the book is challenging, and is far from a simple read. It is very good as a reference book, and for dipping into and out of as required - it''s unlikely you''ll manage to commit to reading the book from start to finish. This is not due to fault of the authors, who do a fantastic job of using engaging and easily-read writing styles, but is simply by virtue of the complicated and vast topics that the book is based on.

In terms of the 4th Edition itself, I would fully recommend this updated version over any of the previous versions. There is a significant amount of new material and improvements compared to the 3rd edition, which helps capture the major developments throughout the past ten years. There are now extensive chapters on Deep learning, probabilistic programming, multi-agent architectures, natural language processing, computer vision and robotics. Furthermore, the book now has a much better glossary with huge range of topics to quickly find, which was definitely a downside of prior editions.

In terms of the physical book itself, I have no major issues with the 4th Edition and can summarise as follows:
- It is huge (1100+ pages).
- It is expensive, but nevertheless a very good investment. It is unlikely another edition of this book will be released for a long time (last version was published in 2010), and so this will stand the test of time for now.
- High quality hardback, with good binding (contrary to other reviews I have read).
- The pages are thin, but nevertheless high quality, with great use of colour on all pages, illustrations and diagrams.
- The book shipped from the US to the UK for delivery, and despite this still arrived pristine without any damage at all. I ordered through Amazon with Book Depository, who packaged it very carefully in a decent sized box with bubble wrap. I know some other sellers might not be so diligent, which isn''t worth taking the chance with such an expensive book.

Overall, I thoroughly recommend. An essential book for the collection of any AI researchers, students, data scientists or AI practitioners.
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T. Gerhardt
5.0 out of 5 starsVerified Purchase
Deeply technical and yet compelling reading.
Reviewed in the United Kingdom on February 14, 2021
Bought for my son who is in year 2 of a MSC in Computer Science with AI. He doesn’t read books. Ever. So what was I thinking giving him a book with over 1100 pages? Well, early days, but so far he has been lounging on the sofa every spare minute—reading!! He loves it. As an...See more
Bought for my son who is in year 2 of a MSC in Computer Science with AI. He doesn’t read books. Ever. So what was I thinking giving him a book with over 1100 pages? Well, early days, but so far he has been lounging on the sofa every spare minute—reading!! He loves it. As an engineer with zero knowledge of AI, I browsed the book and found the writing style easy to read and yet very precise and concise. The chapter intros are easy to understand, the detail in the later chapters goes completely over my head, which is probably a very good thing.
Bought for my son who is in year 2 of a MSC in Computer Science with AI. He doesn’t read books. Ever. So what was I thinking giving him a book with over 1100 pages? Well, early days, but so far he has been lounging on the sofa every spare minute—reading!! He loves it.
As an engineer with zero knowledge of AI, I browsed the book and found the writing style easy to read and yet very precise and concise. The chapter intros are easy to understand, the detail in the later chapters goes completely over my head, which is probably a very good thing.
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Jaume
5.0 out of 5 starsVerified Purchase
El mejor libro de texto sobre inteligencia artificial
Reviewed in Spain on March 22, 2021
Este libro ha sufrido varias reediciones, pero la edición 2020 es simplemente soberbia, es un tratado completo de todas las áreas de la Inteligencia Artificial, completamente actualizada. Los capítulos de Machine Learning y Deep Learning han sido actualizados de forma que...See more
Este libro ha sufrido varias reediciones, pero la edición 2020 es simplemente soberbia, es un tratado completo de todas las áreas de la Inteligencia Artificial, completamente actualizada. Los capítulos de Machine Learning y Deep Learning han sido actualizados de forma que incluyen los últimos avances, y al verificar los autores de cada capítulo se observa que los principales investigadores de cada área han colaborado. Es un libro de texto que da para varias asignaturas trimestrales, y como consulta tiene un valor incalculable. Lo recomiendo a todo estudiante avanzado de informática o matemáticas que quiera entrar en el mundo fascinante de la IA. Ojo. Es un libro de texto, pesa un par de kilos (revisa las medidas porque es un libro grande)
Este libro ha sufrido varias reediciones, pero la edición 2020 es simplemente soberbia, es un tratado completo de todas las áreas de la Inteligencia Artificial, completamente actualizada.
Los capítulos de Machine Learning y Deep Learning han sido actualizados de forma que incluyen los últimos avances, y al verificar los autores de cada capítulo se observa que los principales investigadores de cada área han colaborado.
Es un libro de texto que da para varias asignaturas trimestrales, y como consulta tiene un valor incalculable.
Lo recomiendo a todo estudiante avanzado de informática o matemáticas que quiera entrar en el mundo fascinante de la IA.
Ojo. Es un libro de texto, pesa un par de kilos (revisa las medidas porque es un libro grande)
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Chris Baehr
4.0 out of 5 starsVerified Purchase
Makes other textbooks look tiny. Standard text for software engineering
Reviewed in Canada on March 16, 2021
This book is HUGE. At 1100 pages, it''s an exercise just to hold it and flip through it. The pages are thin but in full color. Everything you wanted to know (and didn''t know you wanted to know) about AI is here. The book covers beginner, intermediate, and advanced topics....See more
This book is HUGE. At 1100 pages, it''s an exercise just to hold it and flip through it. The pages are thin but in full color. Everything you wanted to know (and didn''t know you wanted to know) about AI is here. The book covers beginner, intermediate, and advanced topics. (Not that there really is a "beginner" level to the subject of AI.) This is probably the most comprehensive book ever written on the subject. My only complaint is there are no exercise questions in the book. Everything is online. Having exercise questions helps to reinforce the material being learned. This is the standard text used in software engineering programs in Canada (and elsewhere) for learning about AI.
This book is HUGE. At 1100 pages, it''s an exercise just to hold it and flip through it. The pages are thin but in full color. Everything you wanted to know (and didn''t know you wanted to know) about AI is here.

The book covers beginner, intermediate, and advanced topics. (Not that there really is a "beginner" level to the subject of AI.) This is probably the most comprehensive book ever written on the subject.

My only complaint is there are no exercise questions in the book. Everything is online. Having exercise questions helps to reinforce the material being learned.

This is the standard text used in software engineering programs in Canada (and elsewhere) for learning about AI.
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