Introduction to Machine Learning

Catalog description

This course focuses on the fundamental concepts, theories, and algorithms for machine learning. The course briefly covers topics in supervised, unsupervised, reinforced, and deep learning including clustering, association, regression, neural network, support vector machines, Bayesian decision theory, decision trees, ensemble learning- the nearest neighbor algorithm and random forest.