CS 4210 - Machine Learning and Its Applications (3)

Supervised learning techniques, including linear models for regression, linear models for classification, decision trees, support vector machines, neural networks. Unsupervised learning techniques, including clustering and dimensionality reduction. Related advanced topics. Case studies and applications.

Prerequisite(s): C or better in CS 2410, CS 3010, or consent of instructor.
Component(s): Lecture
Grading Basis: Graded Only
Repeat for Credit: May be taken only once
Course Category: Major Course