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

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

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SKU: DADAX0262018020
Brand: MIT Press
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Webenabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and selfcontained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudocode for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled modelbased approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software packagePMTK (probabilistic modeling toolkit)that is freely available online. The book is suitable for upperlevel undergraduates with an introductorylevel college math background and beginning graduate students.

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