EE 518 Machine Learning Theory and Algorithms
The goal of this course is to provide a thorough understanding of the fundamental methodologies and algorithms used in machine learning. Students will learn to understand, implement, and innovate on algorithms for common tasks such as classification, regression, clustering, and dimensionality reduction. Topics covered include linear and nonlinear regression, bias-variance tradeoff, ensemble methods, support vector machines, K-means, hierarchical clustering, and Gaussian mixture models.
Prerequisite
EE 516 or Instructor Permission.