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Apr 06, 2025
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ELET 6351 - Biomedical Data Mining Credit Hours: 3
Lecture Contact Hours: 3 Lab Contact Hours: 0
Prerequisite: None.
Basic Concepts: Supervised vs Unsupervised Classification; Training Dataset vs. Validation Dataset; Classification vs. Regression; Overfitting vs. Underfitting; Performance: Confusion Matrix, Sensitivity, Specificity, Accuracy, Receiver Operating Curve, Area under ROC; Bayesian Statistics: Bayes’ Theorem, Bayes classifier, Risk and Losses. Supervised Techniques: Parametric Classification: Linear and nonlinear discrimination; Nonparametric Classification: K Nearest neighbor, Decision Trees, Support Vector Machine. u- Basic Regression: Linear Regression, Nonlinear regression. Unsupervised Techniques: Dimensionality Reduction: Linear, Non-linear; Cluster analysis: k-Means. Machine Learning in MATLAB: Data importing; Plotting; Machine Learning Toolbox: Classification Learner.
Repeatability: N
Additional Fee: N Fee Type N
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