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Machine Learning - A Probabilistic Perspective

Users: 2 - Average Rating: 4.50


This book is divided into several chapters explaining different Machine Learning methods and recent approaches in the trend of statistics and probability. These fields have been gaining new insights with recent developments in areas from Bayesian Bayesian Gaussian models to mathematical models. Most procedures are implemented inside a MATLAB package called PMTK.

Type:
Book

Area:
Machine Learning

Target Group:
Basic


Cite as:
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press. ISBN 978-0-262-01802-9

Author of the review:
Inês Sena
Research Centre in Digitalization and Intelligent Robotics (CeDRI) - Instituto Politécnico de Bragança


Reviews

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Pablo Guerrero-Garcia


After several introductory chapters on probability, Bayesian and frequentist statistics, and several regression models, it deals with both directed (Bayes nets) and undirected (Markov random fields) graphical models, as well as mixture, latent, sparse (L1 regularization), state-space, Markov and Hidden Markov models, among others. The book ends with some chapters on inference, including Monte Carlo and MCMC inference. It also provides some background on linear algebra and optimization and a lot of application examples from biology, text processing, computer vision, and robotics.