BLG 527E - Machine Learning

Course Objectives

This course will provide students an overview of issues, algorithms and techniques in machine learning. Students will also gain theoretical and practical experience through programming exercises and projects.

Course Description

Introduction, Cross Validation; Supervised, Unsupervised and Semi-Supervised Learning; Feature and Sample Selection; Dimensionality Reduction; Clustering; Classifiers; Regressors; Ensemble Methods (Combining Multiple Learners); Decision Trees; Linear Dicrimination; Support Vector Machines; Multilayer Perceptrons; Parametric Methods; Multivariate Methods; Evaluation and Assessment Metrics/Criteria