Students will work in small groups and collaborate to find solutions to empirical problems based on real data. Topics cover many uses of AI/ML in the financial sector through practical experiments, including data loading, pipeline orchestration, monitoring, and selective retrying of failed tasks. Emphasis will be placed on data curation, including symbology alignment, the role of fundamentals, and transaction type reconciliation. This will highlight the practical deployment and continuous improvement of models in the financial context.
