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Dec 05, 2025
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CMST 290 - Scientific Programming and Machine Learning Credits: 1
This course introduces students to the field of machine learning. Students learn the algorithms which underpin many popular machine learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. Topics include: supervised learning (regression [simple, multi, and logistic]), (classification [binary, multi, imbalanced]), and unsupervised learning (dimensionality reduction, principal components, cluster analysis).
Repeat for Credit
N
Requisites:
Prerequisite: MATH 100, MLAS 100 or CYBR 103
Typically Offered
Spring
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