Summer Program

Summer Program

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Event Date: August 8th, 2022
Aug. 8-12

Machine Learning: Applications and Opportunities in Social Science Research

This course covers the mechanics underlying machine learning methods and discusses how these techniques can be leveraged by social scientists to gain new insight from their data. Specifically, the course will cover: decision trees, random forests, boosting, k-means clustering and nearest neighbors, support vector machines, kernels, neural networks, and ensemble learning. We will also discuss best practices concerning tuning, error estimation, and model interpretability.

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Summer Program

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Event Date: August 15th, 2022
Aug. 15-19

Machine Learning: Uncovering Hidden Structure in Data

Social scientists are increasingly taking advantage of machine learning methods to gain new insight into their data and expand their methodological toolbox. Indeed, these methods and techniques are revolutionary and indispensable tools for exploring data, learning more deeply about relationships between variables, and ultimately uncovering and visualizing latent or hidden structure embedded in data. This course covers both supervised and unsupervised machine learning methods but will place special emphasis on the (often) underappreciated suite of unsupervised learning tools.

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