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MAT434Statistical Learning and Classification

Course Prerequisites ToolSNHU MAT434 has 1 direct prerequisite, with 4 courses in its complete prerequisite tree.

Undergraduate · 3 credits

Using the foundational knowledge built in MAT 241 and MAT 300, we continue our study of statistical models. This course moves beyond regression and into classification models, mixed models, and unsupervised learning. This course also emphasizes cross-validation as an important method for approximating test error and analyzing the utility of a model. This course covers discriminant analysis, k nearest neighbors, tree-based methods (bagging, boosting, and random forests), support vector machines, and neural networks.

Prerequisite Tree

  • MAT300Applied Statistics II: Regression Analysis
    • MAT240Applied Statistics
    • MAT243Applied Statistics for Science, Technology, Engineering, and Mathematics (STEM)
    • MAT241Modern Statistics with Software

Interactive Prerequisite Graph

Explore the prerequisite relationships visually. The graph below supplements the crawlable list above.

Unofficial — For Informational Purposes Only

This site is unofficial and is intended for informational purposes only. Course requirements, transfer evaluations, catalog rules, and program requirements can change. Always confirm your academic plan with your SNHU advisor for official guidance.