Clojure for Machine Learning

Book Preface

Machine learning has a vast variety of applications in computing. Software systems that use machine learning techniques tend to provide their users with a better user experience. With cloud data becoming more relevant these days, developers will eventually build more intelligent systems that simplify and optimize any routine task for their users.

This book will introduce several machine learning techniques and also describe how we can leverage these techniques in the Clojure programming language.

Clojure is a dynamic and functional programming language built on the Java Virtual Machine (JVM). It’s important to note that Clojure is a member of the Lisp family of languages. Lisp played a key role in the artificial intelligence revolution that took place during the 70s and 80s. Unfortunately, artificial intelligence lost its spark in the late 80s. Lisp, however, continued to evolve, and several dialects of Lisp have been concocted throughout the ages. Clojure is a simple and powerful dialect of Lisp that was first released in 2007. At the time of writing this book, Clojure is one of the most rapidly growing programming languages for the JVM. It currently supports some of the most advanced language features and programming methodologies out there, such as optional typing, software transactional memory, asynchronous programming, and logic programming. The Clojure community is known to mesmerize developers with their elegant and powerful libraries, which is yet another compelling reason to use Clojure.

Machine learning techniques are based on statistics and logic-based reasoning. In this book, we will focus on the statistical side of machine learning. Most of these techniques are based on principles from the artificial intelligence revolution. Machine learning is still an active area of research and development. Large players from the software world, such as Google and Microsoft, have also made significant contributions to machine learning. More software companies are now realizing that applications that use machine learning techniques provide a much better experience to their users.

Although there is a lot of mathematics involved in machine learning, we will focus more on the ideas and practical usage of these techniques, rather than concentrating on the theory and mathematical notations used by these techniques. This book seeks to provide a gentle introduction to machine learning techniques and how they can be used in Clojure.