There is an increasing practical need to understand the foundations of machine learning in the presence of incentives: (i) data ingested by machine learning algorithms are either owned or generated by self-interested parties and (ii) machine learning is deployed to optimize economic systems, like auctions, or to learn how to strategize in economic systems. This workshop will cover topics such as (1) learning with humans in the loop, (2) learning when data providers have a vested interest in the outcome of the learning process, (3) learning and dynamics as a game-theoretic solution concept, and (4) analyzing data (econometrics) from strategic interactions.