Statistical Machine Learning

Abstract

We are living in the golden era of machine learning as it has been deployed in various applications and fields. It has become the statistical and computational principle for data processing. Despite the fact that most of the existing algorithms in machine learning have been around for decades, the area is still booming. Machine learning aims to study the theories and algorithms in statistics, computer science, optimization, and their interplay with each other. This chapter provides a comprehensive review of past and recent state-of-the-art machine learning techniques and their applications in different domains. We focus on practical algorithms of various machine learning techniques and their evolutions. An in-depth analysis and comparison based on the main concepts are presented. Different learning types are studied to investigate each technique’s goals, limitations, and advantages. Moreover, a case study is presented to illustrate the concepts explained and make a practical comparison. This chapterl helps researchers understand the challenges in this area, which can be turned into future research opportunities, and at the same time gain a core understanding of the most recent methodologies in machine learning.