Presented By: Summer Institute in Survey Research Techniques
Machine Learning for Social Science - Summer Institute in Survey Research Techniques
Presented by Brian Kim
Machine Learning for Social Science
Brian Kim
June 3-14, 2024
M/W/F (1:00pm-3:00pm)
Classes are open for registration. You do not have to be affiliated with the University in order to attend.
The mission of the Summer Institute in Survey Research Techniques (SISRT) is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the art practice and theory in the design, implementation, and analysis of surveys.
Space is limited so please register early! Since our courses are not for academic credit, fees are based on the number of assigned “course hours” to each class.
Please view the 2024 course schedule for our extensive class offerings. Classes are offered remotely at their scheduled times.
This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to demonstrate how to tune and evaluate prediction models using the statistical programming language R.
Brian Kim
June 3-14, 2024
M/W/F (1:00pm-3:00pm)
Classes are open for registration. You do not have to be affiliated with the University in order to attend.
The mission of the Summer Institute in Survey Research Techniques (SISRT) is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the art practice and theory in the design, implementation, and analysis of surveys.
Space is limited so please register early! Since our courses are not for academic credit, fees are based on the number of assigned “course hours” to each class.
Please view the 2024 course schedule for our extensive class offerings. Classes are offered remotely at their scheduled times.
This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to demonstrate how to tune and evaluate prediction models using the statistical programming language R.
Cost
- Fees are based on duration of the course. Please see Summer Institute website.
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