Machine Learning Books

The human brain evolved to assist the survival of its owner while the owner navigated the dangerous jungles and forests of ancient times. Its ability to extract patterns from the information provided by the retina and optic nerve is quite phenomenal. The process by which your brain is recognizing my words and understanding my meaning is astounding.

Yet if you are asked to act like a computer by reading numbers, moving paper tape, erasing things and following instructions given on the paper tape, you will prove to be one of the slowest computers in the world. The original word `computer' referred to a man sitting in a room with paper, pencil and eraser. These human `computers' were replaced by machines a long time ago because they are too slow.

In summary, humans are fast and intelligent at being humans but slow at being computers. In the Chinese Room Argument, John Searle states that although we have a human mind which could otherwise be used to understand Chinese, this particular human mind does not in fact understand it. Given this stipulation, the human mind's ability to process language cannot be used and the only method of "understanding Chinese" is left to the "Chinese room" which consists of a computer run by the very slowest of CPUs, the human being sans abacus, sans calculator, sans silicon chips and sans hope.

The Chinese Room Argument is a trick argument that proves nothing. The computer room is so slow that it cannot ever think or understand Chinese. On the other hand, this doesn't say anything about whether a high-speed computer with the memory and processing power of the human brain might one day speak and understand Chinese quite well.

The Chinese Room Argument (CRA) has nothing to do with the speed of computers or any future developments in artifical intelligence (at least as understood as following from Turing). The CRA is a purely formal argument intended to refute the claim that computers (defined as Turing machines) can think, or can understand, or are minds solely by virtue of their formal description. (This claim is the essence of "computationalism," after Turing's original formulation.) The CRA is that: 1) Syntax is not semantics. 2) The implemented synatactical or formal program of a computer is not sufficient to generate semantics. 3) Minds have semantics. 4) Therefore, computers (so defined) are not minds/cannot think/do not understand because they are not sufficient to generate semantics.

For example, the concepts we employ to think and the words we use to speak have meanings. But there is nothing in computationalism as syntax that has any meaning whatsoever. Whatever meaning an implemented formal program has results from its being programmed or interpreted by us. Syntax (e.g., a computer program) has no causal powers. Whatever causal powers computers have (e.g., to fly airplanes) results from our programming and our assigning interpretations to the electrical charge insides a chip, not from the program in itself.

The chapters in Views Into the Chinese Room attack different aspects of the CRA. But they address it as an argument that stands or falls on the truth of the premises and the validity of the inference, not on engineering questions such as the speed of computers, which are irrelevant. Searle believes that there are, in fact, thinking machines -- we human beings are biological machines that think. And he believes that there also could be artificially made machines that think. The CRA is meant to show only that an implemented computer program by itself cannot generate mental content or semantic content.

For a clear explanation of the CRA, see chapter 15 of this book, by Stevan Harnad, the editor of The Behavioral and Brain Sciences, where Searle's original paper appeared twenty years ago. Do not rely on reviewers who do not understand the argument in the first place.

For an introduction to the subject, this book is unequivocal in my experience with the literature. Great read that has propelled me forward into combining a bayesian network with a physical model to approach a very complex sediment transport problem.

I am very pleased to have found a book that gives a modern, sound, and self-contained introduction to Bayesian networks. The only prerequisite is basic knowledge of probability. This makes sense because a Bayesian network is essentially a directed graph whose vertex set is a collection of random variables, while an edge from one variable X to another variable Y represents a belief that X has a causative effect on Y. For example, X could be the pregnancy status of a cow, while Y could be a blood test administered to the cow. Vertex Y would contain a contingency table that reflects the conditional probability of Y in terms of X. The author does well in explaining this, as well as adequately treating many of the practical issues surrounding Bayesian networks, such as design issues, network learing and tuning, and some basic algorithms (e.g. bucket elimination and junction trees) that aid in the efficient updating of variable probabilities due to new evidence that may instantiate or change the distribution of one or more variables.
The author also provides a good introduction to decision graphs, a close relative of Bayesian networks.

The aspect of Bayesian networks that I find most attractive is the fact that there is a "rational" way of designing a network, based on hypothesis, informational, and mediating variables, and their "causal" relationships. Unlike neural networks in which one is almost forced to guess the appropriate structure of the network, every node in a Bayesian network correpsonds with a state or quantity that can be measured either directly or indirectly through other variables. Thus, changes in a system model should only induce local changes in a Bayesian network, where as system changes might require the design and training of an entirely new neural network.

Another aspect of Bayesian networks that I find very compelling is the way in which they seem quite amendable to learning and the presentation of new evidence. This is true since knowledge updating is done locally (through variables), while the effects of those changes are witnessed globally through appropriate belief-updating algorithms.

On the downside, it should be noted that the operation of belief-updating is in general NP-hard, thus there exists a valid concern about the computational efficiency of Bayesian networks. Contrast this with the fact that once a nueral network has been trained, it is quite easy to compute. One would hope that these concerns will subside with more research, for the above mentioned benefits of Bayesian networks leads me to believe that these networks will have quite an influence on the future directions of machine learning.

Although this book will not go down in history as the definitive reference for Bayesian networks, it serves as a good conduit for explaining this quite interesting area of learning at a time when such few complete and modern references exist.

Chapter 1 is a nice introduction to probability. Chapter 2 is readable. Chapter 3 is poorly presented, and you feel sad for having wasted so much money on a book with only one intelligible chapter.

Among currently available introduction to Bayesian networks (also known as Bayes Net, Bayesian Belief Nets), this book is probably one of the most accessible. The book is divided into part I and II. Part I is intended for BN users (practitioners) and Part II more towards BN developers and researchers, as it contains algorithmic introduction of BN.

Prerequisites of the book as stated in the preface include Graph Theory and Calculus, both at introductory level. I personally did not have exposure to Graph theory, but I was able to understand most of the material without any help. Necessary probability theory is developed, but basic probability knowledge is also a prerequisite to digest the material to a reader without prior exposure of Probability as it shapes the core of the material in the book.

The strength of this text is in Part I where the author provides several examples to illustrate use of Bayesian Networks, Influence Diagrams and other models. I find it useful Influence Diagram as an extension of Bayesian Networks.

Most answers to Exercises at the end of each chapter are provided at the author's homepage, except answers of the last chapter. Answers that require graphical modeling software are also provided in Hugin format. (Hugin Lite can be downloaded from Hugin site.)

The downsides are that writing of the text is somewhat awkward, obscuring readers from understanding, that model building chapter could have been discussed more thoroughly, that material in Learning is barely present, and that definitions are sometimes not introduced upon the first encounter but they appear later in chapters. More different and complex examples could have been discussed to illustrate the material. Note: the author provides a page for Learning at his homepage.

Although this is an introduction to Bayesian Networks and Influence Diagrams, a reader should be equipped with some level of abstract thinking in order to digest the material.

This book is suitable for self-study. It has motivations for the uninitiated. References are provided at the end of the book and I was able to find some of them online. A notable is "A tutorial on Learning with Bayesian Networks" by Heckerman, to fill in the part of Learning in this book.

Other books at this level from users' perspective are:
Edwards, Introduction to Graphical Modeling (Utilizes software MIM.)
Clemen, et al., Making Hard Decisions (Uses Palisade Decision Tools suite. The book discusses Influence Diagrams but not Bayesian Networks.)

Further studies after completion of this book include:
Cowell, et al., Probabilistic Networks and Expert Systems
Lauritzen, Graphical Models
Pearl, Probabilistic Reasoning in Intelligent Systems
Pearl, Causality

The book covers many topics, but doesn't really cover them well. I would not recommend this book. I have learned litte from it.

The authors do state in the conclusions that some of the GAs discussed throughout the book do not compare well with the state-of-the-art in place and route tools that have been developed during the last two decades (which is to their credit to mention it.) However, the book makes several very serious mistakes and pitfalls both in the design and implementation of the GAs they try, as well as in the choice of tools to compare their implementations against. For example, in partitioning, they choose for their GA the "obvious" object representation. There has been a lot of work on GAs tailored for "grouping problems" such as partitioning (read Falkenauer) that lead to much better results using an encoding based on "groups representations". Even worse, the algorithms they choose to compare against (standard F-M) is unfair, as there are many F-M based partitioners that beat almost any other algorithm that has been proposed by far!. Not to mention that the benchmarks they use are considered today less than "toy" problems!.
Finally, for a book published in 1999, the bibliography offered is missing a lot of important papers published during the 90's in the fields of physical design for VLSI as well as Genetic Algorithms.

This book describes the application of genetic algorithms to electronics design in a clear, consise, and easy to understand way. Starting with a brief introduction covering terminology and concepts, it quickly moves to applications which facilitate comprehension via example usage. The facet I most appreciate about the book is its ability to apply the technology to real world problems while retaining a close connection with theory. While basic utilization is covered, advanced topics are also presented without sacrifice of detail.

This work specific to electrical engineering, in conjunction with Goldbergs's broader treatment of the general subject, together constitute an essential and complete treatment for both the experienced and learning engineer. I have been fortunate to attend professional lectures by one of the authors (Rudnick) and can attest her clarity of expression and ability to easily cover complex material is present throughout the text.

The author's lucid treatment of the subject makes this the fundamental work on application of GA technology to VLSI design.

I homeschool and bought this book with great enthusiasm. All three projects we attempted were a complete bust. We spent hours collecting all the parts needed, then cutting and preparing. The directions do not go into enough detail and you don't realize, until you have to start over, that something was left out or assumed that you understood. After several attempts and finally getting the projects built, they wouldn't work anyway. My son was off building his own parachute(which worked great) while I was trying to get anything I could find in my kitchen to go up the Archimedes screw!!There must be better resources out there than this completely frustrating book.

My son has checked this book out of the school library so many times he has it memorized! Great fun at home -- inexpensive science projects that really work and are fun to do --- even for Mom! The Rocket created from a 2 liter soft drink bottle and powered by water and a bicycle pump is great. Other favorites include the Archimedes' Screw that moves popcorn and the Rubber Band Powered Boat. Bright, colorful, lots of pictures, easy to understand and easy to build --even for the science challenged!

This book goes for breadth, in a very ambitious take on Petri nets: to cover the complete range of activities of systems engineering supported by nets, from modelling to verification to validation and execution, including case studies in application domains. All this in a complete, self-contained volume. And it mostly succeeds in giving a general view of the possible uses of Petri nets and the research areas related to these nets. So it may be useful to both practicioners and researchers.

The chapters are divided into parts, according to the many aspects investigated: Part I is on basic concepts of Petri nets, including its features, models, definitions and properties. Part II takes on modelling systems with Petri nets. Individual techniques and complete methods (e.g. state-based modelling and event-based modelling) are presented, and case studies analysed. Then, Part III includes four chapters about verification of Petri net models, presenting an array of different techniques and approaches: state-space-based model checking, structural methods, deductive logic-based methods and techniques based on process algebras. Finally, Part IV is about validation and execution of nets, including code generation from net models, and Part V showcases three application domains for the nets: manufacturing systems, workflow systems and telecommunications.

To cover all this ground in the subject of Petri nets, the book was written collectively by more than 20 authors; even some of the chapters are divided into sections written by different people. This naturally results in noticeable variations of style and quality between chapters, and even between sections in the same chapter. Although some effort was spent to try to integrate them better, some sections (and chapters) are quite convoluted and hard to understand, while others are very clear and informative. The notation and style of presentation also changes, but this is mostly not a problem, because chapters often tackle different problems. Also, most of the sections that are difficult to grasp include references that can be used to learn more about the subject. Some sections are really very superficial, covering only the major ideas involved in some technique or method, and sweeping most of the meaty details under the rug, to the references. Unfortunately, some sections don't include enough references to track the original work from which they're based.

Finally, a warning regarding the title: it seems mostly directed to practicioners, but actually includes a lot of material that can be classified as recent research, and so not thoroughly in the field. This is very valuable to a researcher on Petri nets, who can get in contact with a lot of the research on nets done elsewhere, but may be not directly useful to practicioners. Even so, users of Petri nets that are not very interested in research results may take from the book a general idea of tools and possibilities that can be used with net models in all the stages of systems design and execution.

For all readers, I believe this book is mostly useful as a collection of pointers to further research or experimentation.

As far as blueprint reading books this is one of the best I came across. It's easy to read, has practice tests after each section, and covers the subject very nicely!

The book was required for a course, and I found it to be pretty straightforward, if a little dull. It's written at a simpler level than Sipser or Hopcroft and Ullman, which might make it appropriate for someone who is a little apprehensive about the topic (although I think both of those are better books). My biggest complaint was that after finding the first two errors in the text (in an exercise solution and an example), I wasn't comfortable trusting the book to tell me what it meant. The errors were listed in the errata, so if you're using the book, print out a copy of the errata, and mark your book up. Finding these errors for yourself is a good test of your understanding, but it's also more pain than necessary. Use with caution.

I haven't found any reason for someone to buy this book. The writing sytle is dry. The examples are complex and poorly explained. The concepts are covered adequately, but often with a wordiness that leaves the reader bewildered (if still awake). As an "Introduction" manual, this text fails miserably. I'd have given it zero stars, if possible. It just does not do anything well, and does too many things poorly. There's too many well written texts in the world to waste time with this one.

I had to purchase this for my school's Intro to CS Theory course.

Linz' utter ineptitude towards writing is what gives this book 1 star. Examples throughout chapters are sparse and relatively worthless. Sample problems at the end of the chapter, in contrast, are ridiculously difficult, and the solutions in the back don't offer any explanation whatsoever towards the answers.

This is the only book I have ever read that actually made me feel dumber for reading it. It's simply demeaning. Rather than explaining or justifying his logic, as he should to the target audience of this book, he simply uses "it's obvious that..." repeatedly for sample problems and solutions. A ridiculously complex problem's solution in the back of the book will be whittled down to two lines at best, half of which says something along the line of "It's blatantly obvious that the answer is ___, and you're stupid for not realizing it."

If you're actually assigned graded work from this book, may god have mercy on your soul.

This subject is confusing in general, I have this professor and he's really confusing, but when I read his own book it's actually better that him.

Many of the other reviews are negative. I have a nagging feeling that the book was simply too advanced for several, though not necessarily all, of the reviewers.

Look, most undergraduate computer science majors might not need a book as formal as this one. It really is best suited for computer scientists with a strong maths inclination. Many CS students study specific languages, some algorithms, and [hopefully] the hardware of an abstract Neumann machine.

But concepts like the left quotient of a language are really only used by those who want a grand view across all computing languages. And who possibly want to design a new language. This is beyond the capabilities of most CS majors. And so is this book.

This is a very useful book. It starts by introducing very important and interesting techniques in optimisation i.e. genetic algorithms, simulated annealing, tabu search and neural networks. It continues by giving examples of how the techniques have been applied in various case studies. The book also contains code so that users have a head start in implementing the techniques described. This book is good for beginners because it describes the basics of the techniques. It is also suitable for more advanced researchers because the case studies provoke ideas for further work. In conclusion, this book is a useful addition to the bookshelf of any researcher interested in intelligent optimisation.

I bought this book because it has source code for simulated annealing, genetic algorithms, tabu search and neural networks.
I have used the three first source code and ... they are so buggy.
They're wrote in C but doesn't compile due to evident syntax errors (so evident, are they here so as to made these source code unusable ?).
The content of the book is not equilibrate (some metaheuristics aren't discussed thoroughly).
If the authors use there source code, I think evereything presented in this book is completely false.
Don't buy it, there are some better books to buy.

This book gave a superficial coverage on 4 optimization techniques in Chapter 1. Out of the 4 techniques, only Genetic Algorithm was explained slighty more in detail. The rest are merely short examples. That is about all you will get from this book (one brief Chapter on ALL 4 techniques).

I will advice any body that wants to study Computer Numerical Control programming should go for this Bible. What makes it unique is its Chapter 8 that introduces Math for Numerical control Programming and using EVENTS to teach step by step formats. I will not fail to thank the Autor(W. S. SEAMES) for the glossary that helped me a lot to sale through whenever I meet new word. DO YOU WANT TO PASS THIS EXAM? THIS IS THE BOOK.

The cover tells us that the author is a computer systems analyst. This does not qualify him to write about CNC. I suspect that he got his hands on a Fanuc System 6 sometime in the 1980's and declared himself an expert after a few hours of fiddling. I am astonished that the Society of Manufacturing Engineers would put their name on this book which reminds me of Mark Twain's story of sending a boy up a tree to harvest turnips.

There could be a benefit when someone outside the industry looks at what we do with new eyes and ears. It is not as if our own industry's authors are casting their explanation of CNC in a vocabulary of contemporary technological sensibilities. They are stuck in the 1970's and have never been able to explain properly such basic CNC features as interpolation. Coming from nowhere, however, Mr. Seamas flops around with no ability to descern when by accident he happens upon something fresh. He doesn't have the experience (nor, I doubt the prerequisite engineeringing education) to recognize this to do anything with it.

In addition, the book fails badly as a basic primer on CNC. The lowest score an Amazon reviewer can give a book is "1" star, but really, this book is a zero. You are better off with any number of books by other authors.

Sure, the book jumps around a bit, is patchy when it comes to technical details and is fairly poorly referenced, but there's some interesting and inspiring ideas here. However, if you're from a country with a lousy exchange rate with the US (such as poor old Australia) then wait for the paperback edition!

I was very disappointed by this book. He makes a valid point that most machine learning research is concerned with attribute-based (propositional) representations, and that many problems require relational (first-order) representations, but this is not a novel claim.

He calls propositional learning "fence'n'fill" algorithms, because they basically carve up the input space (e.g., a perceptron uses linear boundaries). The advantage is that they are fast and well-understood. In the final chapter, he proposes an algorithm for relational learning which is based on top of a standard fence-n-fill algorithm, but doesn't explain it well, and doesn't give any compelling evidence that it works. The papers on his web site are no better.

He intersperses what little technical material he has with some historical anecdotes about code-breaking during WWII, etc. It's not really clear what the connection is. Overall, the book just does not hang together.

If you felt inclined to buy this book, I would recommend you check out Andy Clark's excellent "Being There" instead.