Program Overview

Program Details

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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 known 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 Information

• Course #: APSTA-GE 2011
• Credits: 2
• Department: Applied Statistics, Social Science, and Humanities

Professors

• Marc Scott: Co-Department Chair, Professor of Applied Statistics; Co-Director of PRIISM

Related Degree

• Master of Science: Applied Statistics for Social Science Research
  • Description: Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

University Information

• Steinhardt School of Culture, Education, and Human Development
• New York University is committed to maintaining an environment that encourages and fosters respect for individual values and appropriate conduct among all persons. In all University spaces—physical and digital—programming, activities, and events are carried out in accordance with applicable law as well as University policy, which includes but is not limited to its Non-Discrimination and Anti-Harassment Policy.