Murphy K.P. Machine Learning: A Probabilistic Perspective

Massachusetts Institute of Technology, 2012. — 1067 p. — ISBN: 0262018020, 978-0262018029.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.Probability
Generative models for discrete data
Gaussian models
Bayesian statistics
Frequentist statistics
Linear regression
Logistic regression
Generalized linear models and the exponential family
Directed graphical models (Bayes nets)
Mixture models and the EM algorithm
Latent linear models
Sparse linear models
Kernels
Gaussian processes
Adaptive basis function models
Markov and hidden Markov models
State space models
Undirected graphical models (Markov random fields)
Exact inference for graphical models
Variational inference
More variational inference
Monte Carlo inference
Markov chain Monte Carlo (MCMC) inference
Clustering
Graphical model structure learning
Latent variable models for discrete data
Deep learning