CIS 4320 - Machine Learning

3 cr
Graded

In this course, the student learns the algorithms, tools and techniques used in modern machine learning applications. Approaches covered include regressions; decision trees and forests of trees; nearest neighbor; and ANNs, CNNs, RNNs, GANs and SVMs. The entire ML life cycle is considered, ranging from exploratory data analysis, data preparation, and proper evaluation of learned models.

Lecture/Lab: 3 hours of lecture
Prerequisite(s): CIS 3050 or CIS 3052; MAT 1530, MAT 1531, or MAT 3210
Typically offered: Fall, even years