Applied Statistical Machine Learning

3 credits · Graduate School

(Also listed as ECE 4363) Application of mathematical techniques that form the foundation of machine learning and artificial intelligence. Probability and statistics, applications of Bayes theorem, matrix analysis, LMS and maximum likelihood estimation. Classification techniques, linear and basis function regressions. Estimation and sampling of probability distributions. Data partitioning and n-fold cross-validation. Recursive Bayesian estimation. Methods of dimensionality reduction. The perceptron, kernel methods, support vector machines, and Gaussian processes. No credit for students who have earned credit for 4363. FALL. [3]