Database Systems Concepts with Oracle CD Review

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(More customer reviews)This book is a requirement for a course that I am taking. The book is riddled with errors. You can randomly open any page from the book- and you will find typos and confusing text. On some pages, the powerpoint slides for the book do not even match with the text of the book. This is the sixth edition of this textbook. I consider it inexcusable that the book has hundreds of typos even after being published for more than ten years. As another reviewer pointed out, the additional material that is needed for the book -like SQL schemas- do not even exist in the book's website- contrary to what the book claims. Anybody who is considering this book should think twice. Please get it from the library - go through the book for a week to see what I mean- and dump the book for any book that should be better than this.

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The Fourth edition of Database System Concepts has been extensively revised from the 3rd edition. The new edition provides improved coverage of concepts, extensive coverage of new tools and techniques, and updated coverage of database system internals. This text is intended for a first course in databases at the junior or senior undergraduate, or first-year graduate level. Database System Concepts, 4th ed. offers a complete background in the basics of database design, languages, and system implementations. Concepts are presented using intuitive descriptions, and important theoretical results are covered, but formal proofs are omitted.The fundamental concepts and algorithms covered in Database System Concepts 4th ed. are based on those used in existing commercial or experimental database systems.The authors present these concepts and algorithms in a general setting that is not tied to one particular database system.

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Learning in Graphical Models (NATO Science Series D: (closed)) Review

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(More customer reviews)The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.
To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.
What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

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In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject underlies the currentvolume. Two research communities which have used graphical or networkformalisms to particular advantage are the belief networkcommunity and the neural network community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and design, theory and applications. Exact methods, samplingmethods and variational methods are discussed in detail. Audience: A wide cross-section of computationally orientedresearchers, including computer scientists, statisticians, electricalengineers, physicists and neuroscientists.

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Artificial General Intelligence (Cognitive Technologies) Review

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(More customer reviews)If you are interested in human-level artificial intelligence you probably should own this book. I liked reading the book and am glad I own it but there are criticisms. Most of the book is too qualitative. Even where prototype software has been deployed algorithms are not given, even in pseudocode. Too much of the book is speculation. I also think that too little attention has been paid to the control of complexity.

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Process Mining: Discovery, Conformance and Enhancement of Business Processes Review

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(More customer reviews)This book is a must-read for everyone who is interested in Process Mining. It covers the fundamentals and basics of this emerging discipline, and it gives a comprehensive overview of the state of the art in the field.
The book is written by Wil van der Aalst, who has started Process Mining research at the Technical University in Eindhoven about twelve years ago, and who has since been in the center of the developments around this new technology.
Be aware that this book is not a practical handbook that explains how to do Process Mining on a step-by-step basis. Instead, it provides a comprehensive overview about the field of Process Mining as a whole. Although the book does not shy away from technical details, it is easy to read. It provides a very good introduction but also highlights the challenges and complexity of Process Mining when dealing with real-life processes. If you prefer to skip the formal definitions you can still get a good overview because there are many concrete examples.

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The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.

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Pattern Recognition, Third Edition Review

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(More customer reviews)I bought this book to teach my students on the subject. I am a professor in computer engineering and PR was not my research focus. However, there are many topics covered in this book, which have become more applicable in our area of research (VLSI design). We found this book easy to use. The algorithms are clearly described and my students could implement them easily by just reading the specific chapters we need. We think this is an excellent book to teach ourselves how to apply various PR algorithms in our domain.

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Information Theory, Inference and Learning Algorithms Review

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(More customer reviews)Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Review

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(More customer reviews)Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

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A general framework for constructing and using probabilistic models ofcomplex systems that would enable a computer to use available information for makingdecisions.

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Petri Net Theory and the Modeling of Systems Review

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(More customer reviews)Thank you for your book, James! It's invaluble source of information necessary to start. It's like "The C Programming Language" by Brian Kernigan, "The art of computer programming" by Donald Knuth. Undoubtly this book is monument in the Petri Nets area. Everyone, who wants to work in the asynchronous systems modelling field, MUST READ this book FROM COVER TO COVER.

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AI for Computer Games and Animation: A Cognitive Modeling Approach Review

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(More customer reviews)When I bought this book I wasn't quite sure what to expect since the title, "AI for Games and Animation: ..." is so broad sounding. What I did expect was that it would be written in an easy to read form and hopefully provide enough in-depth coverage of the topics so that a reasonable game programmer could implement them based on the information provided, without further research. This criteria seems to be adhered to in the technical articles in a publication such as Game Developer.
Apparently, no-one bothered to tell Mr. Funge about this. Although the book does provide some case studies (examples) the actual details of their implementations are often glossed over or shrouded in unnecessary mathematical formalism that is out of place in a game programmer's book. To give an example, complex topics such as inverse kinematics and coupled spring systems are given several short paragraphs. In describing coupled spring systems for deforming a mesh, Funge uses "x dot" notation that most game programmers probably are not familiar with.
The only saving grace is the reference section which can point the reader to more specific literature that may actually be helpful in constructing implementations of some of the techniques described in the book. This increases the rating from the worst (1 star) to 2 stars since it is actually pretty comphrensive.

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John Funge introduces a new approach to creating autonomous characters. Cognitive modeling provides computer-animated characters with logic, reasoning, and planning skills. Individual chapters in the book provide concrete examples of advanced character animation, automated cinematography, and a real-time computer game. Source code, animations, images, and other resources are available at the book's website, listed below.

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Modeling and Reasoning with Bayesian Networks Review

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(More customer reviews)While pursuing my PhD at UCLA, I took Professor Darwiche's classes and had the privilege of using the pre-release version of this book. Before taking professor Darwiche's class, I had spent a good deal of time while working on my masters degree working on Bayesian networks. I found that much of the literature on Bayesian networks was inaccessible to someone new to the field. There simply was not a comprehensive resource that would explain Bayesian networks from the beginning in a through and clear manner. I say with confidence that this has now changed.
The book begins with the fundamentals of logic. It continues on to describe the properties of the Bayesian network graph such as independence relationships and d-separation as well as how the parameters of a Bayesian network work.
There are then in depth discussions of the various queries we are able to perform on Bayesian networks and the algorithms for accomplishing them. These include queries such as probability of evidence, most probable explanation and probabilistic inference. Techniques such as summing out, pearl's polytree algorithm and belief propagation are described elogently and clearly.
The book also contains information on the current state of the art research going on in the field. This book is a valuable resource for anyone new to or ingrained in the use of Bayesian Networks. A book of this scope and target was sorely needed and I for one am glad it has arrived. I would and have recommended this to any of my peers in the field.

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