10-709 Fundamentals of Learning from the Crowd
12 units, Carnegie Mellon University
Crowdsourcing is a burgeoning area that is popular in academic research, industrial applications, and also in societal causes. In this course, we will cover the foundational theoretical principles behind crowdsourcing and learning from the crowd. We will study this field via the lens of game theory how to incentivize people to provide better data and that of learning theory how to make sense of this data. We will also touch upon literature in psychology and economics that studies the behavior of people. Along the way, we will discuss several fascinating paradoxes and conduct some live experiments in the class. Almost all lectures will be taught on the board. Required background material such as scoring rules, Nash equilibrium, concentration inequalities, random matrix theory will be taught in class. Evaluation will be based on homeworks, a final project, and class participation. The prerequisites are basic probability e.g., the student should be comfortable with conditional expectations, the Gaussian distribution, union bound, basic linear algebra e.g., singular value decomposition and basic programming.
Prerequisites: none listed
Courseweave needs JavaScript to show sections, workloads and planning tools.