Introduction to Supervised Machine Learning for Data Science

Abstract

We present an introduction to supervised machine learning methods with emphasis on neural networks, kernel support vector machines, and decision trees. These methods are representative methods of supervised learning. Recently, there has been a boom in artificial intelligence research. Neural networks are a key concept of deep learning and are the origin of the current boom in artificial intelligence research. Support vector machines are one of the most sophisticated learning methods from the perspective of prediction performance. Its high performance is primarily owing to the use of the kernel method, which is an important concept not only for support vector machines but also for other machine learning methods. Although these methods are the so-called black-box methods, the decision tree is a white-box method, where the judgment criteria of prediction by the predictor can be easily interpreted. Decision trees are used as the base method of ensemble learning, which is a refined learning technique to improve prediction performance. We review the theory of supervised machine learning methods and illustrate their applications. We also discuss nonlinear optimization methods for the machine to learn the training dataset.