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Mar 12, 2026
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CMPS 422 - Machine Learning Credit Hours: 3
Lecture Contact Hours: 3 Lab Contact Hours: 0
An introduction to the theory and practice of machine learning with an emphasis on advanced mathematical foundations. Topics include supervised and unsupervised learning, model design and evaluation, regression, classification, clustering, and dimensionality reduction. Students will study core principles such as generalization, bias-variance tradeoff, and maximum likelihood estimation, and implement algorithms including linear and logistic regression, decision trees, random forests, k-nearest neighbors, principal component analysis (PCA), and neural networks.
Repeatable Course: No
Prerequisite(s): CMPS 340 , CMPS 261 , and either MATH 270 or MATH 272 , all with a grade of C or better.
MAX number of credit hours applicable to degree: 3
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