Machine learning consists of designing efficient and accurate prediction algorithms. As in other areas of computer science, some critical measures of the quality of these algorithms are their time and space complexity. But, in machine learning, we will need additionally a notion of sample complexity to evaluate the sample size required for the algorithm to learn a family of concepts. More generally, theoretical learning guarantees for an algorithm depend on the complexity of the concept classes considered and the size of the training sample.

Machine learning, at its core, is concerned with algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present day era of Big Data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information.

Intention of author is to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning.

The book is not a handbook of machine learning practice; instead, the goal is to give the reader sufficient preparation to make the extensive literature on machine learning accessible.