  Automated machine learning (AutoML) aims to find optimal machine learning
solutions automatically given a machine learning problem. It could release the
burden of data scientists from the multifarious manual tuning process and
enable the access of domain experts to the off-the-shelf machine learning
solutions without extensive experience. In this paper, we review the current
developments of AutoML in terms of three categories, automated feature
engineering (AutoFE), automated model and hyperparameter learning (AutoMHL),
and automated deep learning (AutoDL). State-of-the-art techniques adopted in
the three categories are presented, including Bayesian optimization,
reinforcement learning, evolutionary algorithm, and gradient-based approaches.
We summarize popular AutoML frameworks and conclude with current open
challenges of AutoML.
