Artificial Intelligence: A Modern Approach

Written by: Peter Norvig, Stuart Russell

Artificial Intelligence: A Modern Approach Book Cover
For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa
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Artificial Intelligence A Modern Approach Reviews

Ahmed Abd El-Hamid
The most comprehensive book of Artificial Intelligence you'll ever read .. it contains almost everything you need to know about the field
Jethro Kuan
Only done with the first half of the material, but a decent introduction to AI thus far.
Joske Vermeulen
great overview but some chapters outdated
The Discomfort Zone: A Personal History :: Strong Motion :: The Best American Short Stories 1995 :: Making History :: The Deptford Trilogy: Fifth Business/The Manticore/World of Wonders
Fredrik Österberg
A very accessible overview of AI, still being kept up to date and relevant. Requires some basic programming and maths knowledge, but an undergrad shouldn't have any issues if they take the time to read the appendixes about probability and psuedocode.

Some editions (I think the International ones) butchered the contents a bit and should be avoided.
Kai Weber
This book is an awe-inspiring brick already from its outlook, and its content is full of recipes on different levels of abstraction. There are no wonders or miracles here, as a naive reader new to the concepts of Artificial Intelligence might expect. In public discussion the field is often smiled upon, as we humans are still feeling so much superior to Siri or the Google Translator. Yet have you ever wondered what exactly your e-mail-box's spam filter does for you? Or how your credit card provid This book is an awe-inspiring brick already from its outlook, and its content is full of recipes on different levels of abstraction. There are no wonders or miracles here, as a naive reader new to the concepts of Artificial Intelligence might expect. In public discussion the field is often smiled upon, as we humans are still feeling so much superior to Siri or the Google Translator. Yet have you ever wondered what exactly your e-mail-box's spam filter does for you? Or how your credit card provider differentiates legitimate usage from fraud? AI is being researched for a few decades and it is already doing substantial work. This book explains how. It does so in detail and on many different levels of abstraction. To grasp everything introduced here is a challenging task, and requires some knowledge of programming, formal languages, complexity theory, statistics, probability theory, differential calculus, to name a few. If you only know half of these six topics well, then you are like me, and I can assure, that the book still is rewarding, even if you're skipping a few of the calculus parts. You get a first-hand account, a complete overview of the field, you'll learn to choose implementation strategies for different AI tasks, you'll understand what AI tools are doing and if you're a programmer, you'll have enough understanding of the algorithms to be able to implement them by yourself in a given programming language. The book is rounded off with a brief discussion of the ethics of the field (this part could have been a bit longer to my taste; the authors are cutting the moral discussion off a bit too early with saying something like "We're not able to solve this dilemma yet, but there will be a way").
A great and challenging introduction. Ready for take-off.
Gleb
I only read the introduction, the first chapter and chapters 22-24 (the one's concerning natural language processing and computer communication), and bits that I needed to read throughout the rest of the book to understand particular terms.

Of the parts that I read, the authors were very understanding of their audience. I cannot say if the chapters had a sort of natural 'build' to them with every page, but there was a good level of assumed understanding of math and computers that made the text a I only read the introduction, the first chapter and chapters 22-24 (the one's concerning natural language processing and computer communication), and bits that I needed to read throughout the rest of the book to understand particular terms.

Of the parts that I read, the authors were very understanding of their audience. I cannot say if the chapters had a sort of natural 'build' to them with every page, but there was a good level of assumed understanding of math and computers that made the text almost exclusively new information, without re-iterating basic concepts.

Each section stands well on it's own (of what I read), the text does not have a terrible reliance on distant other sections in the book. Not a requirement of course, but just made it much easier to progress through.

The equations were typed out very clearly, and the supporting graphics were minimal and conveyed the point almost instantly.

There were a few sections that seemed out-of-order and/or repeat and irrelevant information, easy enough to identify but misguiding at first.
J
Impressive, and not just physically. Comprehensive introduction to the fundamentals of both symbolic-logical and probabilistic-statistical approaches to intelligence and learning, assuming no prior knowledge beyond basic algorithmic literacy. The chapters on Machine Learning cover much of the same material as Tom Mitchell's Machine Learning, and are a useful complement. As a humanities graduate, I appreciated the contextual historical-bibliographic section at the end of each chapter, and Russell Impressive, and not just physically. Comprehensive introduction to the fundamentals of both symbolic-logical and probabilistic-statistical approaches to intelligence and learning, assuming no prior knowledge beyond basic algorithmic literacy. The chapters on Machine Learning cover much of the same material as Tom Mitchell's Machine Learning, and are a useful complement. As a humanities graduate, I appreciated the contextual historical-bibliographic section at the end of each chapter, and Russell and Norvig's willingness to discuss the broader philosophical questions which frame different versions of artificial intelligence.
Alon Gutman
Very few technical computer science books are so well organize.
I learn a lot, and learning AI really improve you algorithmic thinking.
and bizarre as it might sounds I think concepts like local-search (e.g genetic algorithms ) changed the way I think about the human thinking process. (e.g adding randomness and avoiding local maximum).
Frank Palardy
He could cut a few chapters and focus more on some things. What happens is nothing is covered quite enough while there's so many things covered. Still, it's easier to read than similar books, which can be all math. In other cases there's no math, and just political issues.
Chris Beiser
Full disclosure— I didn't actually read all of this book. But it's really good. Along with CODE, by Charles Petzold, of the only technical books I've read that maintains a technical style, but retains a coherent feeling of exposition as you read it.
Joe Cole
Is a great book. Has many themes, but, the author warns this. The book is a introduction to show you which theme and its way to solve problems is more interesting for you go deeply with other books and materials.
You will learn algoritmos and your difences.
Talha
THE textbook for artificial intelligence. Don't begin a serious survey of AI without this book.
Simon Spero
Norvig and Russel is Essential - alas our household now has two copies of the old edition.
Tanuja
Extremely good intro to artificial intelligence.
Jatinder Virk
A very good introduction to the basics of AI and to some extent to game theory and Logics as well.
Danien
Comprehensive coverage of many AI topics and algorithms. High level approach - don't expect code.
Jiaming Jiang
A great introduction to artificial intelligence. It covers the most basic concepts in AI.
Adam
Provides a broad introduction to the science of AI. Covers a variety of topics and manages to remain interesting. The examples are a little lacking.
Ondrej Sykora
Whatever it is you want to know about AI, there's a good chance you'll find it in this book, well described, within a context and with further references.
Joe
Excellent introduction to AI field. In contrast with other books, the math is presented in an approachable manner and doesn't get in the way of learning the underlying topics.
Arnoud Visser
bigass book on AI, not the most fun thing to read but pretty good.
Adilson Carvalho
Fantastic book. It is a great resource for learning AI.
Alexandru
The big book of AI. I guess every Ai researcher needs to read this. It's a very good reference book that I recommend to everybody that is interested in giving it a start in AI
DJ
I like to explore the possibilities of artificial intelligence.
Barry
A very high quality first introduction to machine learning, I would definitely recommend this text as a companion to Ng's coursera class.
Omar Alsaleh
This book is one of the best in AI. It explains each concept with many examples and figures to get the idea accrossed.
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