Machine learning: a probabilistic perspective
Abstract
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, using 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.
