Machine Learning : a Probabilistic Perspective

Renting vs. Buying This Textbook

Renting is usually cheapest if you only need the book for one term: you return it with a free prepaid label when the term ends. Buying used can beat renting when the sell-back price is high, so compare the sell-back price with the rental price before you decide. An eTextbook arrives instantly but usually expires. Rentals and used copies rarely include working access codes.

Always match the ISBN on your syllabus (9780262018029) before you order; different editions have different ISBNs.

About This Book

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…