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Machine Learning:A Probabilistic Perspective is a comprehensive book for undergraduate and postgraduate students of computer science. The book comprises of chapters on probability, generative models for discrete data, Guassian models, Bayesian statistics, linear regression, logistic regression, generalized linear models and the exponential family, mixture models and EM algorithm and latent variable models for discrete data. In addition, the book is written in an informal, lucid style, complete with pseudo-code for the important algorithms. All topics in the book are illustrated with the help of color images and worked out examples. This book is essential for students of mathematics and computer science pursuing research in probability and optimization. About the Authors Kevin P. Murphy is a Research Scientist at Google. He was a former Associate Professor of Computer Science and Statistics at the University of British Columbia. Robert R. Reitano is a Professor of Finance at Brandeis University's International Business School. He was formerly the executive Vice President and Chief Investment Strategist of John Hancock.