As subtopic of artificial intelligence, machine learning is a mathematically well-defined discipline and usually constructs predictive or decision models from data, instead of explicitly programming them. In this section, you will see some typical examples of where machine learning is applied and the main directions of machine learning.
In this section we explain the basic structure of tabular data used in machine learning. We will differentiate targets from features, talk about labeled and unlabeled data and introduce the concept of the data generating process.
Nearly all supervised learning algorithms can be described in terms of three components: 1) hypothesis space, 2) risk, and 3) optimization. In this section, we explain how these components work together and why this is a very useful concept for many supervised learning approaches.