The explosive growth of online markets has created complex ecosystems of algorithmic agents. To optimize their revenue, agents need to understand how the market works, and to do so they often resort to strategies that learn from past observations. In this course we introduce Online Convex Optimization, the main algorithmic framework for the study of sequential decision- making problems. The first part of the course describes the most important settings and algorithms for online learning. In the second part, we review some recent results characterizing the strengths and limitations of sequential decision-making approaches applied to various problems arising in digital markets. The analysis sheds light on how learning depends on the interplay between the form of the revenue function and the feedback provided during the learning process.
Monday through Friday Lectures: Mon-Fri 10:30-12:30 and 14:30-16:30