This survey overviews a new research agenda on the economics of machine learning, pursued at the Strategic IntelliGence for Machine Agent (SIGMA) Lab at UChicago. This overall research agenda has two themes: machine learning for economics and, conversely, economics for machine learning (ML). The first theme focuses on designing and analyzing ML algorithms for economic problems, ranging from foundational economic models to influential real-world applications such as recommender systems and national security. The second theme employs economic principles to study machine learning itself, such as the valuation and pricing of data, information and ML models, and designing incentive mechanisms to improve large-scale ML research peer reviews. While our research focuses primarily on developing methodologies, in each theme we also highlight some real-world impacts of these works, including ongoing large-scale live experiments and potential deployments for various applications.