An Introduction to Machine Learning
The field of machine learning has a long history and it has been well researched but it is in recent years that it gained major acceptance as computing power has reached a peak point so to make it useful for many applications in the daily life of humans. This document discusses the basic concepts, models and formulations behind the problems of machine learning, influence maximization, and network diffusion. It also analyzes the selected algorithms that were developed to address the above problems. It offers a high level view of the requirements, assumptions and complexity associated with these interesting problems, as well as their connection with real life scenarios. For a more rigorous and in depth analysis and study the reader is referred to the literature that is cited and listed throughout this document. In particular [1] is an excellent reference for machine learning.
Springer International Publishing