9. Machine learning#
In this section we examine some aspects of machine learning (ML) that have connection with Bayesian methods. (There is much of ML that does not have such connections!)
Here are some general explanations from the web about what ML is:
Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. … A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. … Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.
—Wikipedia article on Machine Learning
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
—Ed Burns, TechTarget
To learn more about ML, a great set of resources are the lectures and websites of Morten Hjorth-Jensen, who is jointly faculty at Michigan State University and the University of Oslo. Some of the resources:
Lecture notes collected as a Jupiter book.
Course website with explicit lectures and Jupyter notebooks.