WORKSHOP DAY 2- Machine learning fundamentals for socio-demographic researchers. - Texas Research Data Center, Texas A&M
Friday September 27, 2024 2PM - 3PM
Presented by: Michael C. Lotspeich-Yadao, University of Illinois, Urbana-Champaign
The rapid evolution of computational power, statistical methodologies, and the proliferation of large datasets have spurred a significant rise in the application of machine learning (ML) techniques in various fields. For population researchers, integrating data sources such as social media, mobile phone data, crowd-sourced information, and remote sensing imagery with traditional demographic data has opened up new avenues for predicting population trends and enhancing causal inference.
Despite their potential, the swift advancement of machine learning methods and the associated specialized terminology can make these techniques challenging to grasp, particularly for those more accustomed to traditional research methodologies. At its core, machine learning automates the discovery of patterns and insights from data. This represents a pivotal shift in computer science, where earlier intelligent systems primarily relied on static algorithms—fixed sets of instructions designed to produce specific outcomes from given inputs. These advancements offer powerful tools for monitoring, understanding, and predicting the factors that shape human well-being across different dimensions such as time, space, and demographic characteristics, with applications spanning mortality, health, fertility, social and economic processes, and sustainable development.
This workshop aims to demystify the goals, methodologies, and applications of machine learning within the context of population research. Participants will engage in hands-on tutorials, applying ML methods using R and data from the U.S. Census Bureau. While the course will provide an overview of the theoretical foundations of machine learning, the primary focus is on practical application, equipping attendees with the skills to employ these techniques effectively in their research.