Pattern Recognition and Machine LearningThis is the first textbook on pattern recognition to present the Bayesian viewpoint. It provides an accessible introduction to approximate inference algorithms that allow for fast, approximate answers in situations where exact answers are not feasible. With its use of graphical models to describe probability distributions, this book is a great resource for those with no prior knowledge of pattern recognition or machine learning concepts. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful, though not essential as the book includes a self-contained introduction to basic probability theory.
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(9781138626782) - Edition: 1st edition - Publication date: 26 Sep 2017 - Height: 163mm - Spine width: 30mm - Width: 240mm - Chapman & Hall/CRC - Taylor and Francis - ISBN: 9781138626782 - Number of pages: 364
APress (9781484241486) - Spine width: 16 mm | Image processing, Neural networks, Python concepts, OpenCV algorithms, Image-processing algorithms
A comprehensive guide that blends statistical theory with practical algorithms, covering Bayesian inference, neural networks, and support vector machines to master pattern recognition and machine learning.
(9781138626782) - Edition: 1st edition - Publication date: 26 Sep 2017 - Height: 163mm - Spine width: 30mm - Width: 240mm - Chapman & Hall/CRC - Taylor and Francis - ISBN: 9781138626782 - Number of pages: 364
A comprehensive guide that blends statistical theory with practical algorithms, covering Bayesian inference, neural networks, and support vector machines to master pattern recognition and machine learning.
