Information Nation: Seven Keys to Information Management Compliance Review

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(More customer reviews)I have often found it difficult to engage top IT management with executive management and legal in a comprehensive information management program that can include records management, imaging, document management, and so on. This book provides an easy to read framework for looking at these issues from each of these different perspectives as well as those of Risk Management. In essence, the book provides a set of comfortable "corrective lenses" that let each of these entities share a common vision for the integrity of the organization's information compliance responsibilities.
The authors include many anecdotal narratives in each chapter. These are highlighted with grey background and feature case studies of companies that did or did not manage their information properly and the results of their actions. These break up the chapters nicely and help make the reading go more quickly. The reports of both companies and individuals having been punished will certainly catch the attention of many top executives.
The book is filled with practical steps to get your company's information management compliance program successfully underway. It is easy to read and a great book to share. Highly recommended for anyone involved in Records Management or Risk Management.

Information Nation: Seven Keys to Information Management Compliance

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This fully updated edition demonstrates how businesses can succeed in creating a new culture of information management compliance (IMC) by incorporating an IMC philosophy into a corporate governance structure. Expert advice and insight reveals the proven methodology that adopts the principles, controls, and discipline upon which many corporate compliance programs are built and explains how to apply this methodology to develop and implement IMC programs that anticipate problems and take advantage of opportunities. Plus, you'll learn how to measure information management compliance through the use of auditing and monitoring, following the proper delegation of program roles and components, and creating a culture of information management awareness.

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Independent Component Analysis: Principles and Practice Review

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(More customer reviews)I wish that more math books were written this way. Roberts and Everson give an excellent treatment of this technique, and illustrate some of its most useful variations. It's written in a very academic style and is suited for any graduate student or an undergraduate with familiarity in machine learning. Good linear algebra skills are also recommended. Small and light enough to hold on your lap or read in bed, but carrying all the depth that you'd expect from the seminal papers that pioneered the work. An enjoyable read.


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Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field and includes an extensive introduction to ICA. It reviews the major theoretical bases from a modern perspective, surveys current developments, and describes many case studies of applications in detail. Applications include biomedical examples, signal and image denoising, and mobile communications. The book discusses ICA within the framework of general linear models, but it also compares it to other paradigms such as neural network and graphical modeling methods.

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Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition) Review

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(More customer reviews)This is an good book on machine learning for students at the advanced
undergraduate or Masters level, or for self study, particularly if
some of the background math (eigenvectors, probability theory, etc)
is not already second nature.
Although I am now familiar with much of the math in this area and consider
myself to have intermediate knowledge of machine learning, I can still recall
my first attempts to learn some mathematical topics. At that time my approach
was to implement the ideas as computer programs and plot the results. This
book takes exactly that approach, with each topic being presented both
mathematically and in Python code using the new Numpy and Scipy libraries.
Numpy resembles Matlab and is sufficiently high level that the book code
examples read like pseudocode.
(Another thing I recall when I was first learning was the mistaken
belief that books are free from mistakes. I've since learned to
expect that every first edition is going to have some, and doubly so
for books with math and code examples. However the fact that many of the examples
in this book produce plots is reassuring.)
As mentioned I have only intermediate knowledge of machine learning, and
have no experience with some techniques. I learned regression trees
and ensemble learning from this book -- and then implemented an ensemble
tree classifier that has been quite successful at our company.
Some other strong books are the two Bishop books (Neural Networks for Pattern
Recognition; Pattern Recognition and Machine Learning),
Friedman/Hastie/Tibshirani (Elements of Statistical Learning) and
Duda/Hart/Stork (Pattern Classification). Of these, I think the first Bishop
book is the only other text suitable for a beginner, but it doesn't have the
explanation-by-programming approach and is also now a bit dated (Marsland
includes modern topics such as manifold learning, ensemble learning, and a bit
of graphical models). Friedman et al. is a good collection of algorithms,
including ones that are not presented in Marsland; it is a bit dry however.
The new Bishop is probably the deepest and best current text, but it is
probably most suited for PhD students. Duda et al would be a good book at a
Masters level though its coverage of modern techniques is more limited. Of
course these are just my impressions. Machine learning is a broad subject and
anyone using these algorithms will eventually want to refer to several of these books.
For example, the first Bishop covers the normalized flavor of radial basis
functions (a favorite technique for me), and each of the mentioned books has
their own strengths.

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