Machine Learning A Probabilistic Perspective

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning A Probabilistic Perspective

Kevin P.Murphy

The MIT Press

2012

Abstract

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled 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 self-contained 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 pseudo-code 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 model-based 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 package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

The book contains chapters: Introduction to Probability; Generative models for discrete data; Gaussian models; Bayesian statistics; Freuentist statistics; Logistic regression; Generallized linear and the exponential family; Directed graphical models; Mixture models and the EM algorithm; Latent linear models; Sparse linear models; kernels; Gaussian processes...