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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 #
APSTA-GE 2011
Units
2
Term
January, Spring
Department

Professors

Marc Scott

Professor of Applied Statistics

marc.scott@nyu.edu