James Madison University 3 credits CS
An introduction to the field of machine learning. Explores key problems and algorithms in machine learning from both a theoretical and an applied perspective. Specific topics include supervised and unsupervised learning, anomaly detection, text mining and experimental design. Students who have completed BSAN 393, ISAT 341 and MATH 358 may also take this course. Prerequisite(s): Fully admitted Computer Science majors or minors only and a grade of βC-β or better in CS 327 and either MATH 220 , MATH 229 or MATH 318 .
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