Machine Learning

Abstract

Machine learning is regarded as a subfield of artificial intelligence that deals with algorithms and technologies to squeeze out knowledge from data. Its fundamental ingredient is Big Data, since without help of a machine, our attempt to manually process huge volumes of data would be hopeless. As a product of computer science, machine learning tries to approach problems algorithmically rather than purely via mathematics. An external spectator of a machine learning module would admire it as some sort of magic happening inside a box. Eager reductionism may lead us to say that it all is just “bare” code executed on a classical computer system. Of course, such a statement would be an abomination. Machine learning does belong to a separate branch of software, which learns from data instead of blindly following predefined rules. Nonetheless, for its efficient application, we must know how and what such algorithms learn as well as what type of algorithm(s) to apply in a given context. No machine learning system can notice that it is misappropriated. The goal of this chapter is to lay down the foundational concepts and principles of machine learning exclusively through examples.