Topics in Economics and Machine Learning

Description: Lecture, three hours. Requisites: courses 200B, 201B, or equivalent. Modern developments in information technology lead to deeper engagement between technology and human that involve data, inferences, and decisions between multiple self-interested participants. Theme of blending economics, information theory, and mathematical statistics began to emerge several decades ago with its roots in work of John von Neumann, Jerzy Neyman, Alan Turing, and David Blackwell. New trend in real-world problems solving in industry and science in recent years leads to new interest and progress in this area. Covers machine-learning, game-theoretic, and economic concepts that are relevant across many application domains and on case studies that demonstrate how to apply these concepts and techniques to real-world problems. Topics include two-sided markets (college admissions, dating markets, etc.), auctions (online advertising, spectrum, etc.), social choice, crowdsourcing, reputation systems, equilibrium computation, mean-field game, and mean-field control. Letter grading.

Units: 4.0
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