Reinforcement Learning and Non-Optimal EconomiesFundamentals of reinforcement learning (RL) and comparison with value function iteration.Solving non-optimal economies with RL:Addressing challenges like the absence of Markov properties in equilibria.D
Heterogeneous Agents Models with Machine LearningOverview of heterogeneous agent models in macroeconomics.Using neural networks to approximate distributions in the Krusell-Smith model.Practical implementation and coding exercises.
Introduction to Machine Learning for EconomistsFundamentals of machine learning:Activation functions, neural networks, backpropagation, and loss functions.Pattern recognition as a case study.Application of machine learning concepts to macroeconomic p
The primary objective of this course is to equip students with the computational and statistical tools necessary to analyze and quantify the implications of structural economic models of their choice.
The primary objective of this course is to equip students with the computational and statistical tools necessary to analyze and quantify the implications of structural economic models of their choice.
A panel session with Economics PhD students Beomyun Kim, Angelos Lagoudakis, and Sanjukta Mitra and moderated by Professor John Winters will take place in 368A Heady Hall.
