Machine Learning Books

The authors are young researchers who did their Ph.D. research in this rapidly developing branch of pattern recognition. Because they are young and are at the state of the art in the filed the book has sevral advantages and disadvantages and what I see as a disadvantage someone else might view as an advantage. Anyway here is my view.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that call themselves computer scientists specializing in artificial intelligence. The field is old and has a long history but each discipline has developed their own jargon and many times the wheel has been reinvented. The advantage of this book is that these young scientists don't see that awful history. They have learned and mastered their subject in a basically engineering jargon but they include many concepts from statistics and statistical learning theory that are not common to engineering texts. This includes such topics as robust regression, ridge regression and spline estimation. Much of the classical statistical literature is cited. The book contains over 600 references including much of the authors own work.
Disadvantage 1: Because they are young they miss some of the important historical literature and key texts. I found it a little disappointing that the bootstrap which is a statistical tool that has played a major role in discriminant analysis (particularly in the estimation of classification error rates) was completely overlooked. Also although many important texts on pattern recognition, machine learning and discriminant analysis are cited the fine text by McLachlan is overlooked as is the recent relevant text by Hastie, Tibshirani and Friedman.

Advantage 2: This book highlights the work of Vapnik and Chervonenkis and provides nice concise descriptions that one can easily refer to when needed. The mathematics is deep and includes reproducing kernel Hilbert space and many important properties from functional analysis and statistical theory.

Disadvantage 2: The authors are more experienced at writing professional papers than at writing text books. Consequently the book does not flow well and the authors freely admit in their preface that it is best not to read the book in sequential order but rather to take the suggestions in the preface that differ based on the readers background and interest.

Having said all this, for someone like me, who is very knowledgeable about statistical pattern recognition this is a great text for getting me up to speed on an exciting new area that I know very little about. I became curious about it when I started reading Vapnik recently.

I am hoping that a careful reading of this book will give me an intuition about why this approach that incorporates kernel methods can be a powerful tool in pattern recognition and classification.

This book should be a useful reference for anyone interested in this research area. It could be used in an engineering or statistics course in pattern recognition at either the undergraduate or graduate levels depending on what material is covered.

In a recent communication with Bernhard Scholkopf I learned that his book was sent for publication before the Hastie et al. book went to press. So that is the only reason it wasn't referenced. I think that point is worth my mentioning in an editing of this review. Also on reflection I do not think the disadvantages are so great as to remove a star. So it is 5 stars for them.

I can only hope that they will reference the work of McLachlan and Hastie et al. in their future books and research on this subject.

It is the best book on kernel methods. It covers a wide range of subjects.

The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.

For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the book in the future.

Excellent theory on SVMs and VC dimensionality. However, I found the chapters on optimization a bit terse. Otherwise, an essential reference for those interested in using SVMs in classification and regression.

This book is at the right level if you are already strong in Machine Learning theory. (e.g. Tom Mitchell's "Machine Learning").

Note that it is already getting somewhat dated. It for example includes little information on kernels for discreate structured input, such as trees and graphs.

Great book, but a word of caution, it is not for the novice.
Book assumes a lot of background in functional analysis and
probability. True, it has extensive appendixes but they are
short-handing the relevant materials only. However, having said
that, this is a book worth struggling with even if you have not
yet got the intuitions in the above mentioned disciplines.

It is worthwhile (at least as I can tell) to read the book
skipping the tool chapters (2-6) going back to them when one has
a point where those are needed. I found that to be much easier
as it provides a concrete use of the methods putting them
in context.

I have been looking for this book and I have finally found it. I read this book when I was in the fourth grade and absolutely loved it. Now I am getting it for myself and my 10 year old nephew (does that date me or what?!?) I would highly recommend this book to anyone girl or boy. It's a great adventure story.

ISBN 0064402223 - Not the best thing I've read in kids' books lately, but good enough to hook some of the reluctant readers, especially boys.

Steve and Max are best friends, which is a good thing, because otherwise Max might have killed Steve by now. Who could get away with convincing you to eat a dog treat, if not your best friend? So when Steve comes to the clubhouse with a $2.50 time machine, Max isn't surprised. He doesn't take it seriously, but he's not surprised at all. After some explaining, he agrees to go with Steve to the year 1250... even if the contraption doesn't look like it's going to go anywhere. When Steve thinks Max is getting cold feet, he flips the switch before Max knows what's going on and they find themselves in the bodies of Sir Robert, a medieval knight, and his horse!

Stunned that it worked, thrilled with their success, the boys learn how things work in the Middle Ages. From quack doctors with potions to romance with an Earl's daughter, they're enjoying themselves quite a bit. Now all they have to do is hope the time machine brings them back before one of them is killed!

This is the kind of book that could easily translate into a series, with the boys travelling through time. Since the inventor of their machine, Professor Flybender, went off in search of Atlantis, never to return, there's a story to be told there - and if they tell it, I'll read it!

This is a fun, funny adventure book that no parent should pass up! The authors' quick wit, inside jokes and genuinely funny dialogue make this a great book to take turns reading aloud. The boys get themselves in and out of trouble with clever plans and a healthy sense of humor. Give it a try!

very funny easy read novel following two friends as they travel back in time to the middle ages where they find themselves in the bodies of people/animals who lived at the time.

I remember reading this book when I was young.. and I have read it several times since. It is fun, witty, charming and covers everything from time travel to jousting to courtly love. It's great for boys or girls! A definate must!

I received this book in High School as part of the "Electrical Installation" curricullum in January of 1986. At the advice of my instructor, Mr. Bergovoy, I didn't return the book at the end of the school term. I ended up having to pay for the replacement. However, this book has paid for itself thousands of times over the last 20 years. I recommend it to electricians ranging from the student to the master level..

This book is great. I've read many books on repairing electric motors , but none of them have given me a practical & theoretical view on the subject as this book has. I recommend this book to students, and fully quallified engineers. The best...... My greatest thanks to the authors.

started as a plant electrician and kept learning thanks to this book now repair light fixture to refrigeration

If your looking for a book that is all meat, if your looking for the "bible" of motors, if your desire for motor knoweledge is from the most basic to advance knoweledge.....YOU have to buy this book. I am an electrician, and industrial controls technician and I have found no better book. I am looking through my well used second edition, that was given to me while in college in 1984, and if you could see just my first page of the book, at how much highlighting and notes in the margins there are, you would see that nearly every sentence in the book is packed full of useful information,...IF... you take the time to read it. I believe it is as clearly written as you can get on a technical subject. I consider myself just a slightly above average student, and even I could understand this material. From casual interest in motors to engineer, this book needs to be on your technical bookshelf, unlike other technical books at this price that I threw out after college, this is a keeper. Check out all the other reviewers here, then buy the book.

I am an electrician whose job requires trouble shooting different types of equipment for different customers in many different environments. I see all kinds of motors and machines and jury rigs. This book is invaluable for troubleshooting any motor on the planet. It gives excellent reference schematics and diagrams that coincide with the different chapters. It has helped me on many occasions. It's really meant for poeple who
re-wind motors more than trouble shooters but you will not regret owning it if you do any kind of maintenance or trouble shooting on a variety of machines.

Our consulting firm has successfully collaborated with Dr. Powell for years and I have seen first hand how ADP solves large scale, real world problems that would frankly be intractable by many traditional traditional operations research or optimization techniques. While consulting firms and other business jealously guard their intellectual property, it is terrific for all of us that academics are rewarded for precisely the opposite. I would highly recommend for any serious practitioner to grab a copy of this book and study it. Probably one of the best $100s you will have spent in a while.

In this book Warren nicely blends his practical experience in modeling and solving complex dynamic and stochastic problems occurring in a variety of industries (transportation, the financial sector, energy, etc) with algorithmical and theoretical aspects of approximate dynamic programming. The book can be either used as a textbook in undergraduate or graduate courses, or for practitioners to learn about recent advances in this exciting area. Indeed, I have already used it twice as a textbook for a graduate course, and on the other hand, I have recommended it to several practitioners. Without doubt, this is an important contribution in approximate dynamic programming.

I strongly recommend the book for all practitioners facing large-scale complex dynamic programs. It is also an excellent textbook.

This book represents a paradigm shift in the presentation of dynamic programming/stochastic optimization. Classical treatments of dynamic programming/neuro-dynamic programming/reinforcement learning typically assume small "action spaces," and often assume the presence of a one-step transition matrix. By contrast, authors working with decision vectors in the presence of uncertainty often turn to stochastic linear programming. But these techniques typically struggle when applied to multistage applications. It is extremely hard to solve most of these problems without taking advantage of the presence of a state variable that captures previous history.

I have adopted the notational style where S is the state of the system, and x is a decision, using the language of math programming. x may have many thousands of dimensions for some problem classes (although the book considers many classical problems where decisions are relatively simple).

The challenge that arises when x is a vector when we use dynamic programming is the expectation within the max/min operator. Bellman's equation is typically written

V(S_t) = max (C(S_t,x) + discount * E{V(S_{t+1})|S_t} )

If x is a vector, we generally need the power of math programming to solve the maximization problem. The challenge is the expectation. We avoid this using the post-decision state variable, which is the state immediately after we have made a decision, but before any time has passed (bringing new information). Denoted S^x_t, the post-decision state variable is a deterministic function of S and x. If V^x(S^x_t) is the value function around the post-decision state variable, we obtain

V(S_t) = max (C(S_t,x) + discount * V^x(S^x_t)

The book provides a number of practical examples of this, but the key is that the maximization problem is now a deterministic problem. The final step is that we have to replace V^x() with a suitably chosen approximation. If our maximization problem is a linear, nonlinear or integer programming problem, we have to choose an approximation for V^x() that allows these algorithmic tools to be used.

It is one of the best books about elliptic curve cryptography, taking the reader from the basics of number theory to the elaborate and tricky field of elliptic curves.
It takes into discussion both theoretical and practical aspects of the domain.

When I first opened this book, a tear fell from my eye. Never in my life have I seen such mathematical beauty as summarized from this book. Elliptic curves, isogenies, complex multiplication, higher order abelian varieties, finite fields, point counting, Teichmuller modulus, p-adic numbers, and applications to cryptography: it's all there, and in one amazing book. And the algorithms are written so perfectly that it is easy to translate to the computer language of your choice. Ladies and gentlemen, I promise you, you will not be disappointed by this masterpiece.

Elliptic curve cryptography is now an entrenched field and has been subjected to an enormous amount of research in the last fifteen years. As soon as encryption schemes based on arithmetic in elliptic curves were proposed, it was natural to speculate on whether these schemes could be generalized to hyperelliptic curves or even general abelian varieties. This book gives an overview of what has been done, and even though most of the proofs are omitted, it does serve a need for those interested in the latest developments in the subject. This reviewer did not read the entire book, but concentrated instead on only a few parts that discussed developments in the last few years. Just skimming the book though will reveal that the authors have been very thorough in giving the reader the necessary mathematical background for a study of ECC and HECC cryptography. Readers needing more detailed background can consult the many references.

As expected, a substantial portion of the book is devoted to point counting methods. One of the methods discussed is the p-adic approach to counting the number of points on an elliptic curve over a field with a small characteristic, with the three most practical ones given the most attention. One of these, the Satoh algorithm, first computes the p-adic approximation of the canonical lift of an elliptic curve E over a finite field F(q), where q = p^d and p is a small prime. This involves lifting the j-invariants using a multivariate version of Newton's root finding algorithm. The trace of the Frobenius endomorphism must then be recovered, and this is done by using the action of the lift on a holomorphic differential on the lift. The resulting factoring problems are formidable, so instead the q-th Verschiebung, which is the dual isogeny to the Frobenius endomorphism is used. The Verschiebung is a separable morphism and the trace of an endomorphism is the trace of its dual. These facts are used to express the trace of the Frobenius endomorphism as a product (modulo q) of coefficients in Z(q). These coefficients are then calculated using certain polynomials.

Another algorithm using the p-adic approach to counting is the Arithmetic-Geometric-Mean (AGM) algorithm, which is discussed for the 2-adic case. As the name implies, this method is based on the AGM iteration, wherein a sequence of elliptic curves is constructed all of which are 2-isogenous to each other. This sequence is constructed so that it converges to the canonical lift of an ordinary elliptic curve, and then an explicit formula for the trace of the Frobenius map is derived. It is then shown how to extend the AGM algorithm to hyperelliptic curves by interpreting it as a special case of the Riemann duplication formula for theta functions.

The third p-adic algorithm discussed is called the Kedlaya algorithm and involves working with the affine curve associated to a hyperelliptic curve of genus g. Associated with this affine curve is its `dagger algebra,' the latter of which is discussed in the book and has its origins in the Monsky-Washnitzer cohomology for nonsingular affine curves over a finite field. This cohomology, which is currently listed under the classification of `rigid cohomology' is a cohomology for algebraic fields over fields of nonzero characteristic and can be considered to be a version of de Rham cohomology (in positive characteristic). In arises when one attempts to lift the Frobenius endomorphism on the coordinate ring of the curve to the coordinate ring of a lift of the curve. Taking the p-adic completion of the coordinate ring of the lift results in a de Rham cohomology which is even larger than the coordinate ring (the limit of exact differentials may not be exact), and so one works with a subring of the completion, which is called the `dagger ring.' The Frobenius endomorphism on the coordinate ring can then be lifted to a (Z(q)) endomorphism on the dagger ring. One can then define differentials of elements in the dagger ring, yielding a module over the dagger ring. The kernel and cokernel of this differential map can then be used to construct the zeroth and first Monsky-Washnitzer cohomology groups. The lift of the Frobenius endomorphism to the dagger ring induces an endomorphism on the cohomology groups, and this allows a Lefschetz fixed point formula to be proved, thus giving the number of rational points on the curve. The Kedlaya algorithm essentially follows this approach to do the point counting, but outputting the zeta function and working only for p greater than or equal to 3.

The book is not just a discussion on theoretical developments and computational algorithms, as an entire part of the book is devoted to applications. One of the applications discussed is that of `smart cards' which to date have been one of most widely used applications of cryptography. An entire chapter is spent on the hardware of smart cards, followed by one on how to attack the implementations of cryptosystems. One particular method for extracting the keys from inside a tamper-proof device involves the use of `power consumption analysis,' which is discussed in some detail in this chapter. The power consumption curve of the device or smart card is analyzed by the attacker, and this, coupled with an understanding of cryptographic algorithms, allows the keys to be compromised. Countermeasures against these attacks are discussed in the next chapter. The discussion is general enough in these chapters to give the motivated reader enough information to experiment with both attacking and with designing and testing effective countermeasures.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

The later chapters focus on machine learning. They provide useful introductions into: induction, neural networks, fuzzy logic, and stochastic search. These chapters are particularly useful to workers contemplating how to best perform advanced analysis of complex, large, and possibly imprecise data sets. Consequently, someone contemplating data mining or other intelligent data analysis applications should seriously consider acquiring this book.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This is a hot book! I coached a team of manufacturing managers who worked in a large traditional factory. Our job was to study manufacturing operations in companies that had adopted Toyota's productivity methods and policies. While the men and women on the team had read about lean production, they were disquieted and perhaps even disturbed by obviously highly performing plants that were organized and operated according to principles foreign to their beliefs. At each plant we visited their discomfort deepened. Then, somewhere between the second and fourth visit, each manager had an epiphany. There was some kind of logical reorganization of the manufacturing furniture in their minds and they "got it", as they described the event. Others said, "the light came on." They saw the fundamental logic and sense underlying each lean factory even though each facility assembled pieces of Toyota's productivity methods and policies into its own unique manufacturing system. Interestingly, each member of the visit team became a passionate believer of lean manufacturing. The greatest skeptics became the most outspoken advocates. They called it "getting religion."

People who successfully implement lean manufacturing must be strong believers and must have a personal mental model of lean that functions at the level of a craft - a creative skill for assembling productivity methods and policies into powerfully efficient manufacturing machines. As the great Japanese coaches from Toyota teach Westerners, there is no cookbook, lean is a way of thinking.

The literature on lean production is disappointing. Lean manufacturing books tend to be long dreary laundry lists of productivity methods and technical techniques for quality. There is little available that gives insight into how the great master craftsmen and craftswomen put together marvelous lean machines of production - until now.

This book by Richard McCormack finally brings us face to face with the creative processes of great designers of production systems. Imagine yourself as a novice artist sitting down for a conversation with Auguste Renoir, Vincent Van Gogh, Toulouse-Lautrec or Michelangelo. That is what McCormack brings us in this book - chats with the virtuosos of lean production. Forget those paint-by-numbers books. Either go see the real thing or read "Lean Machines".

A lot has been written about lean, but nothing yet compares to what this book has done.... It's the first time anyone has provided straight answers about the true nature of lean. The author asks the right questions and gets surprising responses. Having spent 20 years in the automotive business, I found this book extremely useful.

This is a hot book! I coached a team of manufacturing managers who worked in a large traditional factory. Our job was to study manufacturing operations in companies that had adopted Toyota's productivity methods and policies. While the men and women on the team had read about lean production, they were disquieted and perhaps even disturbed by obviously highly performing plants that were organized and operated according to principles foreign to their beliefs. At each plant we visited their discomfort deepened. Then, somewhere between the second and fourth visit, each manager had an epiphany. There was some kind of logical reorganization of the manufacturing furniture in their minds and they "got it", as they described the event. Others said, "the light came on." They saw the fundamental logic and sense underlying each lean factory even though each facility assembled pieces of Toyota's productivity methods and policies into its own unique manufacturing system. Interestingly, each member of the visit team became a passionate believer of lean manufacturing. The greatest skeptics became the most outspoken advocates. They called it "getting religion."

People who successfully implement lean manufacturing must be strong believers and must have a personal mental model of lean that functions at the level of a craft - a creative skill for assembling productivity methods and policies into powerfully efficient manufacturing machines. As the great Japanese coaches from Toyota teach Westerners, there is no cookbook, lean is a way of thinking.

The literature on lean production is disappointing. Lean manufacturing books tend to be long dreary laundry lists of productivity methods and technical techniques for quality. There is little available that gives insight into how the great master craftsmen and craftswomen put together marvelous lean machines of production - until now.

This book by Richard McCormack finally brings us face to face with the creative processes of great designers of production systems. Imagine yourself as a novice artist sitting down for a conversation with Auguste Renoir, Vincent Van Gogh, Toulouse-Lautrec or Michelangelo. That is what McCormack brings us in this book - chats with the virtuosos of lean production. Forget those paint-by-numbers books. Either go see the real thing or read "Lean Machines".

What strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.

This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.

I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.

This is a very good book. Another reviewer has commented on Vojislav Kecman being an excellent teacher. I whole-heartedly second that opinion. Often times, while reading this book, you will pause with a doubt or question. What you will find surprising is that almost certainly the author has answered that question in the next paragraph. Many times, the author's answers will tally your own answers.

The first chapter of the book (entitled: Learning and Soft Computing: Rationale, Motivations, Needs, Basics) is 119 pages long. It is an essential reading. By the time you finish reading this chapter the things will start falling into place and you will be more motivated and ready to read the remaining chapters. Until you are highly aware of this topic, do not skip this chapter.

A book is made up of a lot of things other than the text that it covers. Does it contain many/any stupid jokes? Is it printed on the highest quality paper? Is the font size good? Is it printed too dense? Is the cover page inviting enough? Are the dimensions/weight of the book correct? On all these counts the book scores high.

Consistent with the subject matter that it covers, this is not an easy book. You will perhaps like to read it with paper and pencil. But if you are willing to spend time with this book, this book will do a lot of good to you. This is a very good book.

This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).

Book consists of nine chapters, covering SVMs, one- and multi-layer perceptrons and radial-basis function networks, as variants of neural networks, and basics of fuzzy theory. This is followed by interesting case-studies (in financial, control and computer graphic applications) and concluded by basics of optimization theory and an overview of necessary mathematical tools. All the MATLAB programs needed for the simulated experiments are available on the book web site.

Authored by Vojislav Kecman, a prominent researcher in the field of soft computing and previous MIT visiting professor, this book is an excellent material for advanced undergraduate and introductory graduate courses in machine learning applications and soft computing....

This superb book discusses why language was mans most valuable tool ever developed and critiques opinions that say other wise. Enjoy, Mumford was a true geniu

Lewis Mumford was one of the 20th century's most important philosophers, and the two-volume set Myth of the Machine (Volume 1 is Technics and Human Development; and Volume 2 is The Pentagon of Power) are probably his most important books: the summation of his life's work. In writing as elegant as it is clear, Mumford makes plain the death urge that has always underlain civilization, which Mumford calls "the machine," and later "the megamachine." This is a social structure organized not around any organic human needs, but around the "needs" of the machines that have come to characterize and control our lives. These are crucial, incisive, devastating books. I cannot praise them highly enough.

rather than the typical modern myths about mankind and its evolution - evoked by TV-series like "The X-Files" - this great book - as an amazing anthology of the human intellect - shows the "whole truth" ( that is not outhere by the way, but in our brains ) and how much we are going to lose of our human capacilities if we adore the principle of the machine!

Computer Science Students will appreciate this book;
Researchers of different knowledge domains will love it.
The book is a cornerstone in the nowadays culure and a precious
inheritance for the Future.

A highly philosophical book which will be loved my many.

Mathematicians will discover a brand new theoretical
framework for statistical inference.

Computer Scientists will finally learn statistics and then meet
a new model for symbolic and subsymbolic systems emerging
from PAC learning.

Looking at learning issues from a novel prism, this book will
definitely start a series of fruitful discussions and inspire
many researchers.

Having the opportunity to meet the ideas presented in this book
and successfully apply them while co-operating with the authors
in two European Research Projects, I am more than happy to see
them all nicely put together in this book.