An introduction to machine learning that includes fundamental methods, techniques, and applications.
Machine Learning: a Concise Introduction (PDF) offers a comprehensive introduction to the approaches, core concepts, and applications of machine learning. The author — an expert in the field — presents terminology, fundamental ideas, and techniques for solving applied problems in classification, clustering, regression, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more successful and flexible applications. Machine Learning: a Concise Introduction also includes methods for risk estimation, optimization, and model selection— essential elements of most applied projects.
This important resource:
- Contains useful information for effectively communicating with clients
- Presents R source code which shows how to apply and interpret many of the techniques covered
- Includes many thoughtful exercises as an integral part of the textbook, with an appendix of selected solutions
- Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning.
NOTE: This source only includes the ebook Machine Learning: a Concise Introduction by Knox in PDF. No access codes included.