Abstract Machine learning has been widely used in the asset management industry to improve operations and make data-driven decisions. This article provides an overview of machine learning for asset management by presenting various machine learning models in the context of their applications, including general classification and regression, time-series forecasting, natural language processing, dimension reduction, reinforcement learning, data generation, recommendation, and clustering. Additionally, it highlights the challenges of implementing machine learning in asset management, such as data quality and quantity, interpretability, and fairness.