University of Houston 3 credits ELET
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: Bayesβ 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
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