Machine Learning in Survey Research
Friday, 10/25/2019, 9:00am. ARCHIVED EVENT
Location: ISR-Thompson, rm 1430
Please join instructor Adam Eck (assistant professor of computer science, Oberlin College), as he conducts a half-day workshop titled "Machine Learning in Survey Research". This workshop is designed for population/survey researchers and analysts of all skill levels, and will present an introduction to machine learning concepts and their applications to survey research (such as sample frame creation, respondent modelling, and open-ended response coding).
• Introduction to machine learning and its applications to survey research
• Decision trees and random forests
• Deep learning and other neural network-based techniques
• ML techniques to model respondent behaviors, assist with coding of open-ended responses, and more
• Demonstration using R and Python
Presented by the Population Dynamics and Health Program (PDHP).
Adam Eck is an Assistant Professor in the Computer Science Department at Oberlin College. His primary research and teaching interests include: intelligent agents and multiagent systems, machine learning, data science, and computer-aided education.
More specifically, Adam enjoys learning about and developing solutions within decision making under uncertainty (how should agents gather information and behave to maximize rewards in complex, dynamic environments), reinforcement learning (how can agents learn how their worlds' operate in order to guide their decisions), and sequential supervised learning using recurrent neural networks (how can we predict future outcomes based on sequences of past observations).