Machine Learning Fundamentals: Theory and Practice

Undergraduate / Graduate

This course introduces topics in current applications of machine learning for Data Science major students. Topics include machine learning models, supervised and unsupervised learning, loss functions, risk, empirical risk and overfitting, regression and classification, clustering, gradient boosting, decision trees and random forests, and a brief introduction to Neural Networks and deep learning.