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.
School of Engineering and Computing
3 hours of lecture per week