Reviews for Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics)

1. Great Book for mid-adv level students

   Rated 4 out of 5 stars, August 12nd, 2009

   This book is worth whatever you pay for it and I strongly recommend having it in your reference library, esepcially if your field is pattern recognition, computer vision, or neural networks. This book does a good job explaining concepts and gives relevant mathematical insights. It is not, however, written as a tutorial - which in some cases would have been helpful. Also, a bit of code or psuedocode now again would be nice. Otherwise, an outstanding book.

2. Outstanding book

   Rated 5 out of 5 stars, March 12th, 2009

   This is an outstanding book. The author did not neglect any aspect for a good explanation and even a undergraduate student can follow the points in the book. Even though, the book covers as much an introductory view as a more advanced math in a considerable detail, in a clean and simple way. I totally recommend this book.

3. Great Textbook and Reference

   Rated 5 out of 5 stars, March 12th, 2009

   For people that are interested in machine learning this is a must. Not dry and colorful, I really enjoyed the small boxes with the biographies of the great mathematicians, very cool idea.
   
   Advices:
   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.

4. good reference reference book but...

   Rated 3 out of 5 stars, July 12th, 2007

   ..don't like the writing style. Fewer equations and more explanations would have made it a better book.
   
   

5. Great insights, but a hard read

   Rated 3 out of 5 stars, June 12th, 2007

   This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and hence found some of the stuff in the book quite illuminating.
   
   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.