STATS 211
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
Units: 4.0
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Most Helpful Review
Winter 2024 - Xiaowu Dai is the nicest human being I've met. This class is a broad collection of economics topics including auctions, pricing, matching, with a tiny bit of machine learning involved. You can tell the professor really loves this content. He ties every lecture subject with its historical context and active research progress. There are 2 pretty easy homeworks, a midterm presentation, and a final presentation and paper. The presentations are pretty open-ended, you just pick a topic somewhat adjacent to the class that has been published in a top journal and present about it in the midterm. Then for the final, you extend. This is very open-ended. The professor himself is extremely energetic and very open to class participation. He is organized and very dedicated. The subjects themselves are not explored in extreme depth, but the breadth of topics is very fascinating and opens the door for neat discussion.
Winter 2024 - Xiaowu Dai is the nicest human being I've met. This class is a broad collection of economics topics including auctions, pricing, matching, with a tiny bit of machine learning involved. You can tell the professor really loves this content. He ties every lecture subject with its historical context and active research progress. There are 2 pretty easy homeworks, a midterm presentation, and a final presentation and paper. The presentations are pretty open-ended, you just pick a topic somewhat adjacent to the class that has been published in a top journal and present about it in the midterm. Then for the final, you extend. This is very open-ended. The professor himself is extremely energetic and very open to class participation. He is organized and very dedicated. The subjects themselves are not explored in extreme depth, but the breadth of topics is very fascinating and opens the door for neat discussion.
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