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Machine Learning in the Social Sciences:

An Accessible Introduction

with Dr. Jeff Gill

January 12th, 2023
9:30 a.m. - 1:50 p.m. (EST)

Machine Learning in the Social Sciences:

An Accessible Introduction

Thursday, January 12th 9:30 a.m. - 1:50 p.m. (EST)

 

Dr. Jeff Gill

Distinguished Professor in the Department of Government and the Department of Mathematics, American University

 

In this workshop, Dr. Gill will walk participants through the foundations of machine learning and its relevant applications in the social sciences. He will outline how machine learning can be employed to handle big data and introduce you to some of the basic tools one would need to get started, including some of the many data sources from which social scientists might wish to pull data. He will explore the key ethical matters that must be considered, the regulatory frameworks currently in place that limit acquisition and use of data, and some of the ways in which data science through machine learning can help contribute to positive societal changes.

 

Outline

  • What is it and how is it different in the social sciences?

  • Applications in the social sciences

  • Tools for machine learning

    • Software

    • Other training resources

    • Acquiring and generating data

  • Ethics & Regulations

  • Positive contributions and outcomes

  • Preparing for Machine Learning in the Social Sciences: Intermediate

 

Readings & Resources

Gill, Jeff. 2021. “Political Science Is a Data Science.” The Journal of Politics 83 (1): 1–7. https://doi.org/10.1086/711611.

Audience & Prerequisites

This workshop is designed as an accessible introduction. Only an introductory statistics course is a necessary prerequisite. Through this workshop, participants will gain an appreciation of the data-driven approach to research in the social sciences and receive useful insights into its advantages and disadvantages, as well as the various paths available to acquire the necessary tools and the utility of such expertise.

Certificate Credits: 1

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