Instructors: Trent D Buskirk and Adam Eck
Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in complex data structures that are difficult to handle with traditional modeling methods. At the same time, advances in the field of machine learning (ML) have created an array of flexible methods and tools that can be used to tackle a variety of modeling problems. Against this background, this course discusses advanced ML frameworks, methods and models such as regularization methods, ensemble approaches to learning and deep learning models. The course aims to illustrate these concepts, methods and approaches from a social science perspective in an accessible way so that researchers can apply these methods in their own work to unlock insights. Code examples will be provided using both R and Python and will be available to attendees. The course assumes basic familiarity with fundamental machine learning methods like regression, logistic regression and tree-based models.