Introduction to Machine Learning

Catalog description

Feature extraction; Supervised and unsupervised learning; Bias-variance trade-off; Linear, logistic, and nonlinear regression; Decision trees and ensemble models; Neural networks; Deep Learning. Additional work required for graduate students.