Introduction to Machine Learning (3rd Edition)

Machine learning is rapidly becoming a skill that computer science students must master before graduation. This new edition of the book reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for preceptors and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perception; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program.

The book can be used by both advanced undergraduate and postgraduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.