Class Restriction And Registration Summary

 

PSY 40122 - Section 01: Machine Lrng for Soc/Beh Rsch (CRN 20940)
Long Title: Machine Learning for Social and Behavioral Research

Course Description:
Cluster analysis is a statistical approach for the analysis of multivariate data that aims at discovering groups of subjects in a sample that are similar to each other. Clustering techniques are applied in a wide variety of areas including psychiatry (e.g., finding disease categories), marketing (e.g. different consumer profiles), sociology (e.g., social subgroups), etc. Cluster analysis is an example of unsupervised learning. The latter term is derived from the fact that clusters are discovered in the absence of an outcome variable that guides the clustering. Regression trees and random forests, on the other hand, are supervised learning approaches. An outcome such as "case" and "control" is predicted by a number of predictor variables, and the analysis focuses on finding groups with similar response patterns on the predictor variables. In other words, the outcome variable guides ("supervises") finding groups of similar subjects. Outcomes in regression trees or forests can be categorical or continuous.This graduate level course consists of two parts. The first part covers the basics of cluster analysis whereas the second part provides an introduction to regression trees and random forests. The course consists of approximately 2/3 lectures providing the theoretical background, and 1/3 lab sessions, which will use the free software program R. The course is suitable for students with a strong interest in methods. Basic knowledge of matrix algebra and thorough knowledge of regression analysis are a prerequisite. Due to the broad variety of applications of cluster analysis and regression trees, students in (Quantitative) Psychology, Sociology, Political Sciences, or Computer Sciences are equally welcome.

Associated Term: Fall Semester 2019
Campus: Main
Credits: 3
Grade Mode: Standard Letter
Course may not be repeated


Prerequisites:
ACMS 30600 or ECON 30331 or PSY 40120
* Indicates classes which can be taken concurrently



Restrictions:
Must be enrolled in one of the following Levels:
Employee Non-Degree (EM) ,  Graduate Non-Degree (GD) ,  Holy Cross College (HC) ,  St. Mary's College (SM) ,  Undergraduate Non-Degree (UD) ,  Undergraduate (UG)
Must be enrolled in one of the following Campuses:
Main (M)

Registration Availability (Overflow: Off )
  Maximum Actual Remaining
TOTAL 7 4 3



Crosslist Information
Class Information Maximum Actual Remaining
PSY  60122 01, CRN 19739  (Primary) 12 9 3
PSY  40122 01, CRN 20940   7 4 3
MDSC  40122 01, CRN 20994   5 2 3
Total 24 15 9