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May 17, 2024
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EECE 411 - Machine Learning
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: exploring and understanding data; classification using nearest neighbors, using naïve Bayes, and using decision trees and rules; forecasting numeric data using regression methods; neural networks and support vector machines; market basket analysis using association rules; finding groups of data – clustering with k-means; evaluating model performance; improving model performance; and specialized machine learning topics.
Credit 3 hrs May not be repeated for additional credit
Grade Mode Normal (A-F) Course Rotation
Prerequisite(s) MATH 325 , STAT 360 , and COSC 111
Class-Level Restriction Undergraduate standing
Notes -
Updates New Course 5/2017, effective Fall 2017
Summer 2024 Course Sections
Fall 2024 Course Sections
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