Deep Learning for Economists
ECON 2355
Subject & Catalog Number
Course Information
Description
Unstructured data – e.g., text, images, audio, and video – play a central role in economic analyses. Unstructured data are too high-dimensional and complex to use directly in empirical analyses, and instead economists extract low-dimensional, interpretable features. Deep neural networks are the state-of-the-art method for extracting this structured data from unstructured inputs. This course covers state-of-the-art deep neural network architectures and examines a range of applications in computer vision, natural language processing, and multimodal learning. It also covers econometric methods for unbiased, robust, and efficient inference with unstructured data.
Course Notes
This course is for PhD candidates in Economics or Business Economics. Students in other programs may not enroll but may audit if space permits.
Not Available for Cross Registration
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
