ABSTRACT
Machine learning is a collection of techniques designed to detect hidden patterns in data and construct or learn models for predicting the outcome of future data. These predictions can be used to make decisions about the system generating the data in the face of uncertainty. This chapter discusses a mathematical formulation for incorporating insights, intuition, or prior knowledge into the modeling process using informative prior distributions. Prior knowledge can also influence the modeling process. Machine-learning models can often make assumptions about the process or the data. One area of machine learning is focused on learning parametric models that explain the behavior of the data. These types of parametric models often make assumptions about how the data interact with an outcome or the distributions generating the data. Data-driven modeling has become very popular in the last decade. Neural networks and deep-learning techniques have been shown to be extremely powerful in problems such as supervised classification of images.
