DSC 421 - Machine Learning

3 credits
An introduction to four major areas of supervised and unsupervised machine learning: regression, classification, clustering, and dimension reduction. Topics include linear and nonlinear regression methods, k-nearest neighbors, decision trees, and Principle Component Analysis. Types of error such as bias, variance, and irreducible error will be discussed along with their measures including MSE and MAE. Python or another programming language will be used.
Prerequisite(s): CSC 231 , MAT 351 or ASC 351 or concurrent enrollment, MAT 333 or concurrent enrollment