Applied Statistical Machine Learning

3 credits · Undergraduate

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. Prerequisite: One of MATH 2810, MATH 2820, or BME 2400. FALL. [3]