Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.


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.


Machine Learning

Target Group:

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


You have to login to leave a comment. If you are not registered click here

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.