Machine Learning in Astronomy: a practical overview

TL;DR

This paper provides a practical overview of machine learning techniques, especially unsupervised methods, applied to astronomical data analysis, emphasizing their role in extracting new scientific knowledge from large datasets.

Contribution

It offers a comprehensive summary of supervised and unsupervised machine learning algorithms, with practical guidance for their application in astronomy, highlighting the importance of unsupervised methods for discovery.

Findings

Overview of supervised learning algorithms like SVM, Random Forests, neural networks

Focus on unsupervised algorithms for clustering, dimensionality reduction, and outlier detection

Emphasis on the role of machine learning in enabling new astronomical discoveries

Abstract

Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic tools to mine datasets and extract novel information from them. In recent years, machine learning algorithms have become increasingly popular among astronomers, and are now used for a wide variety of tasks. In light of these developments, and the promise and challenges associated with them, the IAC Winter School 2018 focused on big data in Astronomy, with a particular emphasis on machine learning and deep learning techniques. This document summarizes the topics of supervised and unsupervised learning algorithms presented during the school, and provides practical information on the application of such tools to astronomical datasets. In this document I cover basic topics in supervised machine learning, including selection and preprocessing of the input dataset, evaluation methods, and three popular supervised learning algorithms, Support Vector Machines, Random Forests, and shallow Artificial Neural Networks. My main focus is on unsupervised machine learning algorithms, that are used to perform cluster analysis, dimensionality reduction, visualization, and outlier detection. Unsupervised learning algorithms are of particular importance to scientific research, since they can be used to extract new knowledge from existing datasets, and can facilitate new discoveries.