The university continues to monitor the circumstances related to the pandemic. Spring 2021 course offerings are set. However, the course delivery methods and locations are still being updated and will be finalized in the Schedule of Classes by December 4, 2020.
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Courses - Summer 2020
SURV
Survey Methodology Department Site
SURV753
Machine Learning II
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: SURV751; or comparable knowledge or experience.
Recommended: Familiarity with the statistical programming language R.
Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in (para)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 concepts such as cross validation, class imbalance, Boosting and Stacking as well as key approaches for facilitating model tuning and performing feature selection. In this course we also introduce additional machine learning methods including Support Vector Machines, Extra-Trees and LASSO among others. The course aims to illustrate these concepts, methods and approaches from a social science perspective. Furthermore, the course covers techniques for extracting patterns from unstructured data as well as interpreting and presenting results from machine learning algorithms. Code examples will be provided using the statistical programming language R.
Students must have access to a standalone web camera and a headset to participate in the weekly online meetings.