Introduction to Machine Learning
Butch Quinto
Abstract
Machine learning (ML) is a subfield of artificial intelligence, the science and engineering of making intelligent machines.2 One of the pioneers of artificial intelligence, Arthur Samuel, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.”3 Figure 1-1 shows the relationship between artificial intelligence, machine learning, and deep learning. Artificial intelligence (AI) encompasses other fields, which means that while all machine learning is AI, not all AI is machine learning. Another branch of artificial intelligence, symbolic artificial intelligence, was the predominant AI research paradigm for much of the 20th century.4 Symbolic artificial intelligence implementations are referred to as expert systems or knowledge graphs which are in essence rules engines that use if-then statements to draw logical conclusions using deductive reasoning. As you can imagine, symbolic AI suffers from several key limitations; chief among them is the complexity of revising rules once they are defined in the rules engine. Adding more rules increases the knowledge in the rules engine, but it cannot alter existing knowledge.5 Machine learning models on the other hand are more flexible. They can be retrained on new data to learn something new or revise existing knowledge. Symbolic AI also involves significant human intervention. It relies on human knowledge and requires humans to hard-code the rules in the rules engine. On the other hand, machine learning is more dynamic, learning and recognizing patterns from input data to produce the desired output.