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Supervised and Unsupervised Machine Learning

Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into preexisting or know groupings while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analysis. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications.

Course #
Applied Statistics, Social Science, and Humanities


Marc Scott

Chair, Department of Applied Statistics, Social Science, and Humanities; Professor of Applied Statistics

Related Degree

Master of Science
Applied Statistics for Social Science Research

Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

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