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Pattern Recognition and Machine Learning (Information Science and Statistics) Hardcover – 1 Feb 2007
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The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of...
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Top Customer Reviews
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters quite confusing. For those who want to build powerful machine learning solutions to their problems, I am sorry but they will have to look elsewhere. This book cant help you build an application, another serious drawback in my opinion. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning, and not undergraduates or working professionals who want to write machine learning code for their applications/projects.
1) Mathematical background in advanced calculus and linear algebra is required.
2) Basic background in statistics and probability will make the chapters more comprehensible, but if you don't have it, don't despair. The author gives an overview of the required statistical and probabilistic tools in the first chapters and in the appendices.
If you are after a practitioners guide, or a first ML book for self study, this probably isn't ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it's a better first ML book than MacKay (Information Theory ...), Murphy (Machine Learning ...), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning ...) is a good alternative.
Most chapters are fairly self contained, so once you've worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you'd be hard pressed to find a better explanation of either of them. Finally, worth pointing out it's unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you're looking for, this is probably a good choice.
This is THE best book for introduction.
Bishop had a summer school videos on relevant topics on youtube, worth watching over and over again.
Ideas are clear and not too heavy going given the complexity of the topics. very well delivered, concepts well explained, well thought through examples. well formatted too.
Most Recent Customer Reviews
The Springer International Edition (which is cheaper) I believe, has no colour, which distracts a little from the readability. Read morePublished 5 months ago by Paul Driscoll
note that this (cheap) version is in black and white which makes reading the diagrams tricky as the full-price version is presumably in colour. Read morePublished 9 months ago by Amazon Customer
As a Data Scientist I have this book and use it regularly, it is an excellent reference book.Published 10 months ago by CrazyIvan
I've recently purchased this book, but it turned out to be black-and-white despite it was not mention in the description. Read morePublished 10 months ago by Oliver Atanaszov
Great monography about statistic computation and modern pattern recognition. Timeless book.Published 18 months ago by Jan Burianek
This book should be colorful inside, but it is gray colors.Published 18 months ago by Amazon Customer
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