Machine Learning Books

The book is really helpful and was being delivered to me in a timely fashion.

It gives a complete overview and middle-depth discussions on a wide thematic statistics. Additionally provides methodological elements for making decisions on the implementation of specific techniques. Very good book. I'm an economist and statistical and I was very useful.

I'm a machine learning person, and this book provides pretty thorough state-of-art and up-to-date (relatively well) summary of statistical methods being used in lots of pattern classification fields. One thing that does not exist in the book is generative models, although this book is the best of the kind that describes discriminitive models.

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. A comparable text is the text by Mannila, Hand and Smyth.

This book made effective use of color and maintained a competitive price. This had a major impact on publishers like Wiley that could not sell a book at this size and initial price. Wiley is still looking for a book comparable to this one that they can use to compete with Springer-Verlag. I know this information because I heard from the Wiley acquisitions editor that I worked with on my two books.

i really like this book. i haven't finished reading yet. it's extremely dense. by that, i mean every page, every paragraph is packed full of information. it makes for slow but very rewarding reading. i bought the book because

i wanted to learn something about the topic. i've got a math and statistics background, but i haven't dealt with the broad topic of data mining or statistical learning. the book suits my needs very very well.

it's clearly written. i haven't found any grammatical or technical errors. it's pacing is ambitious, but i find i can follow it. i do think some math and statistics background is required to make the book readable and useful.

i wouldn't hesitate to recommend it to someone with the appropriate background.

The material is very superficially laid out and for a book with the word "Practical" in the sub-title it contains almost no practical examples of data mining.

Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books.
Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many.

This book was pretty good. I have to admit that for the first hundred or so pages, I was feeling very impatient. All of that information could have been conveyed in about 25 pages, and been much easier to read. But there are some very good examples in here, and it is worth reading. If you are looking for something more technical, try "Pattern Recognition and Machine Learning", by Christopher M. Bishop or "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman.

I am very happy with amazon purchases as they always come quick, as described. I love the free supersavings shipping program. Prices are charged in the middle (not the chepest, not the highest) but I know I can always rely on Amazon! Every time I have something to buy online, I go to Amazon.

This book reminds me of the programming books by Deitel&Deitel. It's wordy and superficial, making lots of people feel like they understand the subject. Unfortunately, it takes *much* more than what's in this book to really understand Data Mining. Compare this book to the book by Hastie, Friedman and Tibshiranie, which really goes into the statistics involved in Data Mining.
There is no magic: real Data Mining needs lots of Statistics. You can learn to use Weka, but in order to do real work you'll need to understand what goes behind its nice user interface, and I think this book is not enough.

This book covers data mining at a serious level, including essential material on testing and a broad array of techniques. It is written for practitioners and provides clear explanation of included topics. Easily one of the best 5 books on data mining currently available.

Note that this book has moved on to a second edition.

I have bought this book as course book to learn some particular aspects of data mining.
With the software that you can dowload you can do yourself all the exercices for every models presented
It's the best way to progress
Do the same, it's simple and funny
The explanations are very clear and pedagogical, very practical

This book is actual a textbook for a data analysis course. We use it because the flow of the chapters is almost the same as the flow of the course material. Unfortunately, it is not as useful as expected if you are in the field. It is not in depth for the materials that the authors wanted to cover due to the fact that this is not a book for just programming or just statistics. If you have a strong background on machine language or a strong background on data analysis, you may not find it useful for you career. This book is for those who have limited knowledge on both programming and statistics, but not for professionals.

Witten's book, combined with the accompanying open source package, Weka, provides a great overview of data mining principles and practice from a machine learning perspective. One could hardly do better than to own this book and "The Elements of Statistical Learning; Data Mining, Inference, and Prediction" by Hastie, Tibshirani, and Friedman which covers complimentary material from the statistician's perspective. Witten does an amazing job of providing a comprehensive overview of the field while still providing some depth re. the algorithms; after reading the book I didn't feel like I'd read yet another large volume of empty claims about the power of information technology to make me rich and famous. In fact, with the book by my side, in a relatively short time I was able to use Weka to pry some useful information from one of my medical imaging data sets (maybe even enough to serve as preliminary data for a grant application). It seems to me that with an understanding of the material in this book and the one by Hastie et. al. one could embark on serious data mining projects.

I've been working with "big name software" for some years, but when I joined the institution I work now and no tools where available I begun my quest for an open source tool that could help me build statistical models applied to real business problems.

As a result of this quest I found the WEKA data mining software on the Internet (you can find it on www.cs.waikato.ac.nz/~ml/weka/) and that nice piece of software leaded me to this book.

This book is EXCELLENT and I am giving 5 *five* stars to it as it helped me understanding the whole process of datamining: from loading the data to building the model.

I've read some reviews and I think some of them are not fair (particularly one that says that this book have "just words with no relation or sense at all").. THIS BOOK IS REALLY WELL WRITTEN but you have to read it slowly: As when you study something.

Buy this book (*don't forget to download the software*) and I am totally sure that you will be producing and using models in a week.

Can't imagine that some weeks ago

Cheers,

I have to agree with all of johnnied7 criticisms. This book is pitched at a level too advanced for an introduction. It also reads and is structured like a research paper. Not recommended.

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

ISBN 0064471403 - Books that have gotten multiple awards are usually surrounded by such an aura of "You HAVE to like this book!" that I find my expectations too high. Slot Machine seemed to be one of those - A Best Book for Young Adults, Young Adult Choices for 1997, Winner of a 1995 Bulletin Blue Ribbon, and A Recommended Book for Reluctant Young Adult Readers... that's a lot to live up to! To my surprise, it does!

Elvin and his friends go to a summer retreat known as Twenty-One Nights with the Knights. Elvin's got a negative outlook from the start and things immediately live down to that perspective. Not, in any way, athletic, Elvin struggles as he is pushed to fit into a "slot". Everyone, they tell him, has a slot - and all of those slots are athletic. After proving that he's definitely not an athlete, and making an unpleasant discovery about the "in" crowd guys, he reaches bottom. And he finds that he likes it there! He's not alone, and it's clearly his "slot".

I can see why Slot Machine would get a Reluctant Reader award - Elvin might be more of a misfit than the reader, but almost every young adult FEELS like a misfit at some time! He's a funny character, with a bit of the smart-aleck. Lynch does a really nice job with all his characters, leaving me wishing for a peek at next year's Twenty-One Nights with the Knights.

Chris Lynch packs a whallop in Slot Machine. Any young adult who has searched for his slot, his place to "fit in", will identify with Elvin Bishop. Readers will cry, moan, and eventually laugh with Elvin as he suffers through defeat, rejection, and then triumph. Lynch manages to sum up many readers' entire adolescence in three weeks of an action-packed, bully-infested, humiliation-filled summer camp. And yet our hero, Elvin Bishop, emerges stronger than he went in. He is secure in his knowledge of who he is, even as he watches his two best friends lose a bit of their perfection. Even though this tale is a bit tall, Lynch's writing contains realistic dialogue and character traits that young adults will identify with. Slot Machine is the epitomy of "coming of age" books, leaving readers a little more secure in the knowledge of who they might become.

The book that I read was Slot Machine by Chris Lynch. This book is about a boy named Elvin who is 14 years old. Elvin and his friends decide to go to this camp over the summer because they had nothing else to do. The camp was called 21 Nights with the Knights and it was for mainly overweight people. Once they got there they found out that they had to pick a slot. A slot meant that you would pick a sport and try to get better at it over the summer. Then Elvin found out that some of his teachers were there from school to help. The first slot that Elvin picked was football and the first day of practice he got hurt. After he was in the football slot, he got moved to wrestling and baseball slot. You will have to read the book to find out the ending.

One thing that I liked was when they were on the bus fighting over the cookies because I really like cookies. Another thing that I liked was when Elvin got hurt and met another friend. Then Elvin found out that you could only get three injury papers to see the nurse. The thing that I didn't like was when Elvin kept getting yelled at and moved to different slots.

I would recommend this book to people who like sports books and some conflict between the characters. This book is for boys who are in grades from seventh to tenth grade. Another similar author that I found to recommend would be Matt Christopher.

I read the book Slot Machine by Chris Lynch. It is an adventure/comedy book and is very entertaining and I never wanted to put down the book. I choose this book because when I looked at it it seemed interesting because of the title of the book. I loved the book because of the predicament that the main character was in. Also, there was kind of a diffrent story on the side which included one of the main characters two best friends. I would recomend this book to people from the 6th grade to the 10th grade. Chris Lynch has written other books including the Shadow Boxer and Iceman. Lynch has a masters degree from the writing program at Emerson College.
This book is about a boy named Elvin who is going to a new school. He is at a retreat where all the freshmen go to find what "slot", sport, they are in. Unfortunately Elvin can't find himself a slot. For a side story he has two best friends. One of his best friends Frankie was trying to fit in with the older and cooler kids. His other best friend Mike found his slot easily and is like there father, he kept encouraging Elvin to find a slot. In one part of the story Frankie trys to pursuade Mike and Elvin to come with him to a party with the older kids. Frank says "You guys want another chance tonight, the O's kind of think you two are wimps". After Frank said that I wonderd if Mike and Elvin were going to fold under peer pressure, but they did not. That is just on of the parts of this excellent book. I recomend if you are between the age groups I suggested that you definetely read this book.

The plot of the story is that some kid goes to camp and is bad at every sports and at the end he is in the very weird art group and did not change at all durring the book. A day in the life of some guy that sleeps all day would be more interesting than this. I BEG OF YOU DO NOT READ THIS BOOK IT HAS NO POINT. Everyone who writes reveiws are parents who love the book but the kid hates it. I am a kid and I know how the person who is reading it feels

I purchased this book for my about-to-be 4th grader. I liked the format of everything written in the first person-i.e. each of the four characters writes a narrative. The plot ended up being a little thin and the ending was a let down.

My biggest objection was the multiple uses of the word "sucks" and at least one "freaking" (as a substitute for the "F" word). Maybe it's acceptable for teens to use these words on a regular basis but I didn't feel it was appropriate for a 9 year old. I discontinued this book with my kids after 2 chapters for this reason. Call me old fashioned but I just thought other parents might like to know the content.

As other reviewers have mentioned, the treatment of the war was a little much for this age as well.

Kids in grades 4-5 will love this book! It is a bit too easy for middle school kids, but a fun read none the less. It is written unlike most other chapter books. It is written more like an interview conducted with each character as the school year progresses and problems arise with Belch. This book can teach valuable lessons on ethics and morals. This is a definite read for my students this year. If you are a child who does not like to read or are a teacher or parent who knows kids who are reluctant readers, Dan Gutman books are fabulous. My students all rave about them.

The homework machine is a great book it is about a group of kids who become friends. They become friends by using a machine made to do homework codenamed Belch. The book is very very funny and knows just when to add humor to it. The only bad thing about this book is that you wish the book was longer.

I think this is a good book for kids and grown-ups alike. You should read this book as soon as you can, so you can read it before people start talking about it and you look like a dummy. Read it today!

The book had a few clever bits of writing which might appeal to young readers, however the format made for very fragmented characters. It does not deserve the Sunshine State award. I also object to the paragraph written about Sam's father: "He died for nothing." The paragraph is only a few sentences and is really the only reflection of feeling from the characters when this major event happens. It is an abrupt explaination of a very complex situation and completely inappropriate for this reading level. If the author wanted to venture into the subject of US foreign policy then he should have either written it with less cruelty and more background information or written a book for a more mature reader. It is possible for our young students to understand the seriousness of war and the political areana that surrounds it, however, we must educate them with facts from all sides of the issue and encourage healthy debate. This cruel paragraph was clearly the author's simplistic hostility or at the very least a lazy author not able or willing to fully handle a sensitive and important subject.

This book is about four kids who create a homework machine to do they're homework for them, but is this a gift or a curse? Their classmates begin to get suspicious and attempt to get them to admit it, but will one of them crack?

I enjoyed how everyone gets to express they're own point of view throughout the book, and how the author introduces different characters throughout the book. It's a shame that it didn't last longer, but then again I didn't stop reading it so that would be why.

The homework machine is one of my favourite books of this genre the layout is fun to read, Dan Gutman has exceeded himself in this book. I like how people that are completely different gradually over time become friends. This was one of the greatest books I've read in a long time. You have to read this book, beacause it is a brilliant read that will interest your kids and most likely yourself.

The book is a little dry at times. Also, I didn't get a very clear idea of how to select kernel functions, which seems pretty important.

The book is just great. The appendix on algorithms could have more explanations. Also the application section is a short. It would have been more usuful to take one of these applicaitons and describe it in details. But all in all, the book is excellent.

This book introduces the concepts of kernel-based methods and focuses specifically on Support Vector Machines (SVM). It is hard to read and a good background in mathematic is clearly needed. The book has a strong emphasis on SVM starting from the very first line of text. Concepts are well explained, although equations are not clear. The notation doesn't facilitate the reading at all. The book covers linear as well as kernel learning. The kernel trick is well described. It is easy to understand ideas behind SVM while reading the corresponding chapter. Finally a small chapter on SVM applications is proposed. Unfortunately, it only contains typical SVM applications (i.e. standard problems).

I think this book is good if you:

* Have a strong mathematical background
* Work in the specific domain of SVM (or kernel-based methods in general)
* Want to write a research paper about SVM and need the correct notations

However, this book is NOT intended for people who:

* Don't like to read theorems, corollaries and remarks
* Are not interested in reading hundreds of proofs

This is my personal opinion as a computer scientist: this book is definitely written for mathematicians.

I just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John Shawe-Taylor. Could not resist adding my own comments about the book. Excellent book. I plan to use the book for the course on "Fundamentals of computer aided engineering" that I teach at the Swiss Federal Institute of Technology, Lausanne (EPFL).

Oh Puhleeeezzzzz... How is your vector math??? Remember your linear algebra well? Do you have a background in SVM's? Intuitively able to suck out of thin air the meaning of the Gamma co-efficient as applied to svm's?? You've read all the background papers and remember your formal logic???? No?? too bad..your out of luck..

This book is more aptly titled an Introduction to the Formalisms of SVM's. If your a software engineer trying to implement one of these, forget it.. Be nice if they put that quadratic algorthim psuedocode into something more readable than greek symbology..

If you are trying to build one of these engines, then this book is of absolutely no help, unless you have a background in machine learning and have read all the papers on SVM's. If you can decompose the math into code in your head, then you might find it entertaining... What I don't get is how all the rest of these reviewers can give such "glowing praise" for this book and have it be so completely worthless as an introduction... makes me think some of these are shills..

Bottom line is, if your trying to code a svm, this book will not help. If your trying to understand how to implement a svm, this book will not help. If you are trying to understand how an svm works, this book will not help. If you want to know the mathematical basis for SVM's and like that presentation.. this is the book for you..

This is a great book intended for practitioners and "light" theoreticians. It contains precise explanations that show how to use the cross entropy method efficiently for both estimation of rare events and for optimization. The code is valuable and covers a large variety of applications. The book is deductive and easy to follow, and not cluttered with too much notations.

I really liked the applications chapters - easy to follow and show what all the fuss is about. Seems like the kind of book you'd like to have around if you're actually solving optimization problems.

This book provides an excellent introduction to the Cross-Entropy (CE) method, which is a new and interesting method for the estimation of rare event probabilities and combinatorial optimisation.

The book contains all of the material required by a practitioner or researcher to get started with the CE method. The fact that accompanying Matlab code is freely available renders this field especially accessible to new-comers.

The book has a strong practical flavour, and is easy to read. It will be of interest to anybody working in the field of Monte-Carlo simulation and/or stochastic optimisation.

The cross entropy method (CE) is a modern technique attacking optimization and estimation problems by simulation. It has been introduced by the first author and it is elaborated thoroughly in this book. The reader will find a lucid introductory chapter into the subject followed by the core of the book consisting of a chapter where CE returns an iterative algorithm for adaptive importance sampling simulation, and a chapter where CE is transformed into a randomized algorithm for solving combinatorial optimization problems. The book concludes with several chapters with applications including detailed numerical results and some Matlab codes.

I read the book with great pleasure because it is a well written exposition of a fascinating method containing many illustrative examples and realistic applications. I think that it is appropriate for both practitioners and theorists in simulation and optimization. While reading the book I got encouraged to apply CE to several other problems because the CE basics seems so simple while the results are marvellous. I am interested specifically in rare event simulation so I focused on reading the simulation part where I found many inspiring new ideas. In fact, I applied CE to a reliability problem and obtained results far better than existing methods. The simulation chapter is the most mathematically oriented, for instance it gives a proof of convergence and it contains recent developments in simulation of rare events with heavy tails.

I can recommend this book to everyone who likes to learn new ways for solving estimation and optimization problems.

Although Cross Entropy is a relatively new methodology in optimization, there has seen an "explosion" of new articles offering theoretical extensions and new applications in the last few years. Hence, this book comes just in time to review the state of the art and help "new comers" enter this field. The method is presented in a clear, easy-to-follow manner and the best part of the book, in my opinion, is the focus on several areas of application where tough problems were already solved with CE. I have recently used this book to support a novel CE application to project management and found it extremely useful. I think it should become a standard piece in the "tool-box" of both scholars and practicionairs interested in optimization.

The cross-entropy method is an exciting new technique for rare event simulation and stochastic optimization.
The book unfortunately is a 99% copy and paste of the public available tutorials and papers. I bought the book before looking in the internet, so do not do the same mistake by me.

Furthermore it is quite disappointing if every chapter is written in a highly redundant manner(which follows automatically if every chapter is a paper on its own).

The topic and method is great but the book doesnt add much what the papers wont tell.

Exellent study guide for beginner and journeyman Accompanying CD is articulate and the best I have seen.The author has done a fine job,both technically and artistically.

Very competently written text that covers all areas of traditional motor control. The author demonstrates his field knowledge by including typical characteristics of device failure and indications where inspection and possible maintenance would be prudent. Circuits and schematics are shown in standard ladder logic convention and in some instances even shows analogy to digital logic for those who are formerly trained electronics technicians.

My one criticism is the sequencing of some of the chapters. The author seems to have weaved in and out of some areas where I expected more complete coverage only to find the topics further toward the back of the text. This detracts from an otherwise well written and authoritative text for the 1st time and intermediate level learner.

For those who want to know the fundamentals of Automated Industrial Systems under the heading of Electrical Motor Control... This text of ISBN 0-8273-8640-0, I can not recommend. There maybe better text that I am not aware of, But ISBN 0-8269-1663-5 from 1987 is a text you can grow with. I'll be upgrading to ISBN 0-8269-1675-9 by Gary Rockis & Glen Mazur. Sincerely, Industrial Maintenance Electrician of 12+ years. I will post an additional review once I have viewed the upgraded edition. (I've wondered about these reviews... Are they posted by a PR / sales people???) Good Luck & Be Safe.

Well written electronic/electrical books are like finding gold nuggets. I have an electrical background and used this book as a refresher to study for a professional test. I found it was well written, accurate and to the point. The authors did not get bogged down in endless calculations, but did present what was necessary. The authors covered each subject completely with well selected words, illustrations, and pictures that conveyed the idea in a simple to understand format. I would recommend this book to anyone, beginner, or craftsman seeking review of subjects they have not worked with for some time. I liked the short unit concept of covering each subject. I will look to these authors first for other electrical titles in the future.

This book is loaded with informative and valuable essential
information that anyone in the field would find enteresting, and would be of extreme benefit to those entering the field.

I found each chapter well written, easy to understand, and not cluttered with useless information.

The Unit on "Synchronous Automatic Motor Starter" was of particular interest to me as I have operated these machines before.

The section on "Developing Control Circuits" was also exciting for me as I have worked on storm drain pumping stations, and sewage lift stations which utilized the two pump alternating scheme shown in Figs. 2 thru 6.

The solid state theory and circuits was excellent.

This book makes for great reading, and I recommend it for all hands.

Suchman's book is a classic (and about to be updated!), but that doesn't mean there aren't any caveats. Suchman's analysis is deep, her writing thick (incredibly terse, dense prose that may require a good dictionary), and her perspective is still controversial.

This book doesn't tell you how to "do" very much - it's not a step-by-step method book. This is a mix of theory and method that will force the engaged reader to reflect on his/her own work.

This book stands as perhaps the best example of a socio-cognitive analysis of technology, and is therefore correctly treated as fundamental in HCI and related fields. For a researcher who is interested in the relationship between technology and people, or technology and the world, this is a must-read. AI and HCI stumble into each other frequently, but this is a book for both audiences.

As for the debate of plans vs. situated action, well, to some extent I find it irrelevant. Suchman never claims that plans don't exist or are unimportant. Even if your work is completely plan-oriented - say, AI planning (e.g. path planning), you should read this book - it will challenge some of your assumptions, and force you to grapple with problems that exist when technology interacts with the world.

That having been said, this is not an introductory reader on HCI, AI, or any other topic. Suchman's terse language frustrates even some very intelligent grad students and PhD's, and again, this book is deep. It's a book that has challenged me as I've read and re-read it over the years, and I treasure it.

This book is not for everyone. Suchman makes connections between AI, HCI and the sociological areas of ethnomethodology and conversation analysis (EM/CA) - connections that have been very visible and influential in subsequent HCI and CSCW research. If you don't have any background in these sociological areas, it will take some work to read it.

That said, I think this book is reasonably accessible, and certainly more so than has been suggested by some reviewers. Suchman was writing to counter a prevalent mindset in the AI community of the time. Basically, Chapters 2 and 3 set up a technical and philosophical strawman (human action as the execution of plans), Chapters 4 and 5 provide an explanation of some necessary theoretical background, and the rest is an analysis of interaction in the context of these theories that serves to knock down the strawman. It's fairly hard to have a more clear and logical organization than that. There's no part of that organization that could be left out and still have the book make sense.

Furthermore, by comparison, the theoretical parts of this book should be easier for the uninitiated to read than are Garfinkel's writings on ethnomethodology (or most CA writings by almost anyone). They may or may not do justice to those ideas, but that's a separate question. And for someone with any background at all in these areas (though as suggested by other reviewers, this does not include a huge number of people), this book should be a very straightforward read.

The bottom line for me is that this book (like Paul Dourish's "Where the Action Is") is an interdisciplinary gem that has the potential to change how you think about how people approach technology. There aren't that many books for which that can be said.

If you do read it, read only the last chapter and the conclusion.

Summary:
Keep in mind that the title of the book is Plans and Situated Actions: The Problem of Human Machine Communication. The majority of the book is the 'plans and situated actions' part.

The basic idea of the book is that humans don't really function using plans. Plans, as the author defines them, are something akin to diagrams for behavior, explicating specific activities. Instead, the author argues that humans behave based on 'situated actions'. Situated actions are, "the view that every course of action depends in essential ways upon its material and social circumstances. Rather than attempting to abstract action away from its circumstances and represent it as a rational plan, the approach is to study how people use their circumstances to achieve intelligent action." (p. 50).

In other words, people have a goal in mind. To achieve their goal, people may or may not set up a plan (the author discusses how this could be culturally relative, but I think this is a weak point in her argument because she doesn't really do a good job of distinguishing one type of plan from another), but what is important is that in trying to achieve their goal they are placed in situations that determine their actions. This could also be said: people behave in specific situations based upon the factors that affect the situation.

Let me give an example... Let's say your goal is to get to the dentist. You set up a 'plan' for getting to the dentist prior to leaving. Your plan would include a calculation of the time and the route and your mode of transportation. The situated action approach would say that you can only understand the individual's behavior in terms of their actions in specific situations. So you get in your car and on the way to the dentist's office you run into a detour due to construction. If you had to follow your plan, you couldn't make it to the dentist. But when you leave the road and find an alternate route, this behavior is only understood in terms of situated action. Does that explain it? Wow, and it only took me a few paragraphs.

The author discusses plans and situated actions in terms of conversations, cognitive science, ethnomethodology, and a whole bunch of other theoretical perspectives and technical jargon. In the end she finally gets to the human and machine communication. This is also where the book begins to get interesting. She studied how people interacted with copy machines that were trying to give people instructions. Her studies, undoubtedly helped the people at Xerox figure out ways to improve their copy machines and instructions for them. Like I said above, the last chapter and the conclusion are the most interesting parts of the book. Skip the rest and read them.

My Comments:
For someone so concerned with understanding how people communicate this book is horribly written and nearly unintelligible. The first six chapters are theory and examples of the theory that are completely unrelated to machines. The book finally gets to human and machine interaction after nearly one hundred pages of inchoate theory. And the human and machine interaction stuff isn't really all that interesting - especially since it predates the 1990s, is talking about interaction with copying machines, and has nothing to do with computers.

The author should have chosen a specific approach and then stuck to it. Perhaps she could have tripled the length of the book and gave clear and understandable explanations of the theories (though I am pretty much convinced after having read the book that this would be impossible because of the author's writing style) and used examples that applied only to human and machine interaction. Or she could have just jumped into her findings that dealt with human and machine interaction. The first approach could have been 'dumbed down' to make the book readable by the general public. The second approach could have served a more academic market.

The book reads something like a doctoral dissertation (it very well may be one, I don't know) in that she gives some information on each theory, but not really enough to give someone a good understanding of it - something like a literature review - and cites examples of research that are completely unrelated to the topic of the book to illustrate the theories . The she presents her methods, results, and conclusion.

I guess my problem is that I was expecting a book that would actually be enjoyable to read, interesting, and would focus on human and machine communication. If that is what you are looking for, look somewhere else. This book is nearly impossible to understand. I read the book for a graduate level course in Ethnomethodology and I didn't really understand it very well. By no means am I an expert in Ethnomethodology, but I'm pretty sure I know more about it than probably 95% of the world's population (keep in mind I don't know very much at all), so I'm pretty confident most people would find this book nearly impossible to decipher.

This is an outstanding book. The insight that showed the power of the idea of `situated action' goes far beyond the realm of interactive design or even human computer interaction in its entirety. It is a fundamental solution to the problem of facing complexity and contingency. Its implications are widespread. This book was published in the 1987 when during the last days of classical AI. This is one of the seminal books that showed the inadequacies of the classical formulation. Indeed it showed a new and much more way of achieving the goals that classical AI set for itself and failed. Despite its age the ides in this book are still fresh and important.

. Absolute certainty is impossible and the quest for it is costly and futile. Instead of trying to overcome the uncertainty that is in the world, the system designer should embrace it and use it as a tool to solve the problems that it creates.

This is a book that should be read by anyone who has set the task for themselves of developing any system that must function in an uncertain environment. In short this is a book that should be read by anyone who is developing a system that will have to function within the real world

This is THE book to start with if anybody is interested in studying interaction design. In a time everybody calls themselves an interaction designer, it's a highly recommended reading to learn there's more to interaction than simply large colourful buttons... Based on an ethnomethodological perspective, Suchman does a brilliant job in analysing users' interactions with an advanced Xerox machine, and putting forth an interesting critique of classical AI concepts. It's highly recommended for anybody interested in Human-Computer Communication and interaction design.