  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.
