ELET 6351 - Biomedical Data Mining

Credit Hours: 3.0
Lecture Contact Hours: 3.0 Lab Contact Hours: 0.0

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
Additional Fee: N Fee Type N