Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Kevin P. Murphy


Today's Web-enabled deluge of digital info demands automatic equipment of information research. desktop studying presents those, constructing tools that could immediately become aware of styles in info after which use the exposed styles to foretell destiny info. This textbook deals a complete and self-contained advent to the sphere of desktop studying, in response to a unified, probabilistic method. The assurance combines breadth and intensity, delivering worthwhile history fabric on such issues as chance, optimization, and linear algebra in addition to dialogue of contemporary advancements within the box, together with conditional random fields, L1 regularization, and deep studying. The e-book is written in a casual, available sort, entire with pseudo-code for an important algorithms. All themes are copiously illustrated with colour photographs and labored examples drawn from such software domain names as biology, textual content processing, machine imaginative and prescient, and robotics. instead of delivering a cookbook of alternative heuristic tools, the ebook stresses a principled model-based method, usually utilizing the language of graphical types to specify versions in a concise and intuitive approach. just about all the types defined were carried out in a MATLAB software program package deal -- PMTK (probabilistic modeling toolkit) -- that's freely to be had on-line. The publication is acceptable for upper-level undergraduates with an introductory-level collage math heritage and starting graduate scholars.

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