Artificial Intelligence Books

Network+ Guide to Networks, Fourth Edition (Networking)

Tamara Dean

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Customer Reviews:
Total reviews: 20 Average rating: 3.5 of 5

Grueling Course 5 out of 5 stars.
12 of 13 people found this review helpful.

This book is officially approved courseware for the CompTIA Network+ exam. It has been approved also by the CSSIA (Center for Systems Security and Information Assurance). The book is very comprehensive. But as all textbooks go it has both strengths and weaknesses. In this review I will try to highlight some of both.

Like most computer industry books, this one comes with a supplemental CD. That CD includes the Certblaster and MeasureUP self-test software and other files the student will find useful.

Each chapter begins with a highlight box that will outline what will be covered in the chapters. Then it includes a letter or story from someone working in the industry. At first these seemed a little cheesy, but as I moved further and further into the book I realized that they were a great source for a feel for the material in the real world, and not just in the classroom or self-study. On the side of the pages are some green bars; these bars highlight specific information you are required to know for the CompTIA exam, and which exam objective they are part of.

One of the greatest strengths is that each chapter ends with a mini glossary of key terms or new terms from that chapter. There is also a complete glossary of all of these terms at the back of the book. Then at each chapter's end there is a series of review questions - multiple choice questions much like those found in self-test software and on the actual exam. Finally, each chapter has a series of hands-on projects. Each project gets progressively more difficult.

The greatest strength of this book is that it has all-encompassing hardware, software, different OS's. The weakness is that it is a lot of material packed very densely into nearly a thousand pages. When I did the school program, we did this book in 3 weeks. For most it was a little overwhelming.

If you want to write the CompTIA Network+ exam, this is a great book to help get you there. Pay special attention to the Novell Netware sections. There were a lot of questions on the exam about those. And Good Luck!

Editorial Review:

Network+ Guide to Networks, Fourth Edition is designed to prepare users for CompTIA's newly-revised 2005 Network+ certification exam and will also offer mapping features to the exam objectives.

Artificial Intelligence: A Modern Approach (2nd Edition) (Prentice Hall Series in Artificial Intelligence)

Stuart Russell, Peter Norvig

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Customer Reviews:
Total reviews: 80 Average rating: 4.0 of 5

Disappointing... 2 out of 5 stars.
5 of 8 people found this review helpful.

Following the accolades in the reviews and having a keen interest in AI (as a physician and computer scientist) - I have dived into this book. It took me more than half a year of stubbornly trying to read and understand it. What a disappointment...
On one hand, the math is inaccessible, least you have a major in computer sciences / statistics, math - or all of the above. It seems some, if not all of the math "proofs" are unnecessary for the matter at hand. Unless there are some sinister motives behind these superfluous math complications - such as providing professors with ammunition for students testing. But why should someone interested in AI - get bogged down in this? Is it really what the authors had in mind?
On the other hand there are not enough examples to follow and the examples that are there - are inconsistent and insufficient (for example: the `wumpus' world that is used in the logic chapters, actually succeeds to stir an interest in the reader and then ....it is not followed up in the subsequent chapters such as the one on Bayesian networks)...
Some easy to grasp principles (such as basic propositional logic) are repeated ad nauseam while some difficult subjects (such as MCMC) are left as puzzling axioms, for us to decipher on our own.

I summarize my disappointment asking myself what I got from this effort that I have invested into this book, absorption and digestion wise, professionally speaking:
1. Did this book help me better understand the depth and breadth of the AI domain? - No.
2. Am I able to develop, even conceptually a plan for an AI application / "intelligent agent"? Absolutely not.
3. Did the book clarify for me the fields of logic, machine learning, reasoning, uncertainty, probability and so on? - No. I am as confused now as I was before embarking on this study project, maybe even more so.
4. Am I a smarter person, able to read now the multitude of scientific articles out there on the AI subject - after finishing this book? - No.

The only reason I gave it 2 stars instead of the single one it deserves - is because of the historical and bibliographical summaries the authors have nicely detailed at the end of each chapter. I've seen other books recommended in these reviews - and I intend to look into them shortly. CAVEAT EMPTOR (buyer beware) !

Editorial Review:

Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning.

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

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Customer Reviews:
Total reviews: 34 Average rating: 4.5 of 5

Editorial Review:

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

The Singularity Is Near: When Humans Transcend Biology

Ray Kurzweil

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Customer Reviews:
Total reviews: 124 Average rating: 4.0 of 5

487 pages + a good editor = 225 pages 3 out of 5 stars.
1 of 1 people found this review helpful.

It's a potentially important book as many other reviewers have pointed out but his stream of consciousness writing style gets aggravating. If you can skim, you win. If you read, you bleed. There aren't many things he says fewer than four times. But some of those things have come true, some will and some of the amazing ones may. He has an impressive track record. Now if he'd just add discipline to his writing.

Sure, Ray, I'll take your word for it... 3 out of 5 stars.
1 of 1 people found this review helpful.

Futurists are seductive and so are their fantastical predictions, even when one has absolutely no idea exactly how to evaluate the soundness of their claims. Kurzweil tries with all his might to answer this criticism of the genre but fails nonetheless, offering mound upon mound of at best incomplete graphs that bury his theses behind the madness of immeasurable technological erudition, so (alas) the reader is probably left to do one of two things: ignorantly object or ignorantly serve. It's good fun, much like a fireside game of "what if" at summer camp, and Bill Gates's official endorsement makes it feel populist enough to recommend to your inquisitive friends.

Editorial Review:

For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.

Godel, Escher, Bach: An Eternal Golden Braid

Douglas R. Hofstadter

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Total reviews: 239 Average rating: 4.5 of 5

Editorial Review:

Twenty years after it topped the bestseller charts, Douglas R. Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.

Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Borrowing a page from Lewis Carroll (who might well have been a fan of this book), each chapter presents dialogue between the Tortoise and Achilles, as well as other characters who dramatize concepts discussed later in more detail. Allusions to Bach's music (centering on his Musical Offering) and Escher's continually paradoxical artwork are plentiful here. This more approachable material lets the author delve into serious number theory (concentrating on the ramifications of Gödel's Theorem of Incompleteness) while stopping along the way to ponder the work of a host of other mathematicians, artists, and thinkers.

The world has moved on since 1979, of course. The book predicted that computers probably won't ever beat humans in chess, though Deep Blue beat Garry Kasparov in 1997. And the vinyl record, which serves for some of Hofstadter's best analogies, is now left to collectors. Sections on recursion and the graphs of certain functions from physics look tantalizing, like the fractals of recent chaos theory. And AI has moved on, of course, with mixed results. Yet Gödel, Escher, Bach remains a remarkable achievement. Its intellectual range and ability to let us visualize difficult mathematical concepts help make it one of this century's best for anyone who's interested in computers and their potential for real intelligence. --Richard Dragan

Topics Covered: J.S. Bach, M.C. Escher, Kurt Gödel: biographical information and work, artificial intelligence (AI) history and theories, strange loops and tangled hierarchies, formal and informal systems, number theory, form in mathematics, figure and ground, consistency, completeness, Euclidean and non-Euclidean geometry, recursive structures, theories of meaning, propositional calculus, typographical number theory, Zen and mathematics, levels of description and computers; theory of mind: neurons, minds and thoughts; undecidability; self-reference and self-representation; Turing test for machine intelligence.

Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop

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Customer Reviews:
Total reviews: 40 Average rating: 4.0 of 5

Editorial Review:

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

The Big Switch: Rewiring the World, from Edison to Google

Nicholas Carr

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Total reviews: 29 Average rating: 3.5 of 5

Editorial Review:

An eye-opening look at the new computer revolution and the coming transformation of our economy, society, and culture.

A hundred years ago, companies stopped producing their own power with steam engines and generators and plugged into the newly built electric grid. The cheap power pumped out by electric utilities not only changed how businesses operated but also brought the modern world into existence. Today a similar revolution is under way. Companies are dismantling their private computer systems and tapping into rich services delivered over the Internet. This time it's computing that's turning into a utility. The shift is already remaking the computer industry, bringing new competitors like Google to the fore and threatening traditional stalwarts like Microsoft and Dell. But the effects will reach much further. Cheap computing will ultimately change society as profoundly as cheap electricity did. In this lucid and compelling book, Nicholas Carr weaves together history, economics, and technology to explain why computing is changing—and what it means for all of us.

Guide to Networking Essentials

Greg Tomsho

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Customer Reviews:
Total reviews: 10 Average rating: 3.5 of 5

Not for self-study! 2 out of 5 stars.
6 of 7 people found this review helpful.

Please be aware that this book (and others by the same publisher)are meant for colleges and/or trade schools and thus the answers to exercises, labs, projects, etc are available ONLY to instructors. Furthermore, neither the publisher nor the authors maintain a website for errata so you are on your own if you misunderstand a concept due to typos and false information.

Editorial Review:

Guide to Networking Essentials, Fifth Edition has been revised and provides a baseline level of knowledge for success in industry and preparation for networking certifications, including the MCSA, MCSE, CNA and CCNA designations. This new edition has been updated to cover new industry topics such as Networked Attached Storage (NAS), Cable Installation and Management, as well as, Fixed and Mobile WiMAX. Written with a focus on networking operating systems, this book continues to provide up-to-date coverage of Microsoft Windows XP and Server 2003 technologies along with UNIX, Red Hat Linux, and Novell Netware operating systems.

The Unofficial LEGO MINDSTORMS NXT Inventor's Guide

David J. Perdue

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Total reviews: 12 Average rating: 5.0 of 5

Editorial Review:

The LEGO® MINDSTORMS® NXT set includes a variety of powerful electronic parts (motors, sensors, and the programmable NXT microcomputer) and lots of building elements. With these parts you can create thousands of unique, intelligent robots. The Unofficial LEGO MINDSTORMS NXT Inventor's Guide helps you to harness the capabilities of the NXT set and effectively plan, build, and program your own NXT robots.

The first part of the book contains a brief introduction to the NXT set. You'll then examine the pieces in the NXT set and the roles they play in construction. Next, you'll learn practical building techniques, like how to build sturdy structures and work with gears. Then it's time to learn how to program with the official NXT-G programming language (and learn a bit about several unofficial programming languages). You'll also learn what author David J. Perdue calls the MINDSTORMS method: a strategy for creating your own robots. Finally, you'll follow step-by-step instructions for building, programming, and testing six sample robots (all of which can be built using only the parts found in one NXT set). These six robots include:

• Zippy-Bot, a two-wheeled vehicle with a ball caster
• Bumper-Bot, a version of Zippy-Bot that explores your room and avoids objects
• Claw-Bot, a version of Zippy-Bot that searches for objects and pushes them away with its claw
• Tag-Bot, a four-wheeled steering vehicle that dashes around while you try to "tag" it with a flashlight
• Guard-Bot, a six-legged walking robot that launches a ball at intruders
• Golf-Bot, a stationary robot that finds a target and then hits a ball into the target

You'll also find an NXT-G Quick Reference, which provides information on the 30 types of standard programming blocks, and the LEGO MINDSTORMS NXT Piece Library, which features detailed information and images of the more than 80 types of LEGO pieces in the NXT set.

So go ahead. Grab your NXT set, fire up your creative mind, and see what you can invent with The Unofficial LEGO MINDSTORMS NXT Inventor's Guide.

The Elements of Statistical Learning

T. Hastie, R. Tibshirani, J. H. Friedman

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Customer Reviews:
Total reviews: 25 Average rating: 4.0 of 5

Editorial Review:

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.