  Machine learning (ML) methods have been developing rapidly, but configuring
and selecting proper methods to achieve a desired performance is increasingly
difficult and tedious. To address this challenge, automated machine learning
(AutoML) has emerged, which aims to generate satisfactory ML configurations for
given tasks in a data-driven way. In this paper, we provide a comprehensive
survey on this topic. We begin with the formal definition of AutoML and then
introduce its principles, including the bi-level learning objective, the
learning strategy, and the theoretical interpretation. Then, we summarize the
AutoML practices by setting up the taxonomy of existing works based on three
main factors: the search space, the search algorithm, and the evaluation
strategy. Each category is also explained with the representative methods.
Then, we illustrate the principles and practices with exemplary applications
from configuring ML pipeline, one-shot neural architecture search, and
integration with foundation models. Finally, we highlight the emerging
directions of AutoML and conclude the survey.
