Introduction to Machine Learning

3 credits · Graduate

Introduction to core concepts in machine learning and statistical pattern recognition, with a focus on discriminative and generative classifiers (nearest-neighbors, Bayes, logistic regression, linear discriminant, support vector machine, and relevance vector machine). Dimensionality reduction and feature selection. Classifier performance evaluation, bias-variance tradeoff, and cross-validation. Prerequisite: (Mathematics 216, 218D-1, 218D-2, or 221, or ECE 586) and (Computer Science 201 or ECE 551D) and (ECE 480 or ECE 581 or MATH 541 or MATH 730 or MATH 740). Not open to students who have taken Computer Science 671D.