Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) Hardcover – 16 Nov 2009
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"This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning. As such, it is likely to become a definitive reference for all those who work in this area. Detailed worked examples and case studies also make the book accessible to students."--Kevin Murphy, Department of Computer Science, University of British Columbia
About the Author
Daphne Koller is Professor in the Department of Computer Science at Stanford University. Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University
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Top Customer Reviews
The book is graduate level and needs the reader to have solid skills in linear algebra, probabilities and statistics to make the most of it.
What I really like about this book is the fact it only focus on one topic: Graphical models and do not try to cover Machine Learning in general. The consequence of that is its thorough treatment of many aspects of graphical models which is rare in the literature. That's why I highly and warmly recommend this book.
relevant chapters in Pattern Recognition and Machine learning by Bishop might be an easier starter, and you might learn more insight by just reading through. Come back to this book as this has much more detailed treatment, but be warned, it is very dry.
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