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.

Reviews

This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.—British Computer Society—

About the Author

Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

1 Introduction 2 Probability 3 Generative models for discrete data 4 Gaussian models 5 Bayesian statistics 6 Frequentist statistics 7 Linear regression 8 Logistic regression 9 Generalized linear models and the exponential family 10 Directed graphical models (Bayes nets) 11 Mixture models and the EM algorithm 12 Latent linear models 13 Sparse linear models 14 Kernels 15 Gaussian processes 16 Adaptive basis function models 17 Markov and hidden Markov models 18 State space models 19 Undirected graphical models (Markov random fields) 20 Exact inference for graphical models 21 Variational inference 22 More variational inference 23 Monte Carlo inference 24 Markov chain Monte Carlo (MCMC) inference 25 Clustering 26 Graphical model structure learning 27 Latent variable models for discrete data 28 Deep learning Notation Bibliography Indexes