Machine Learning Technologies

Catalog description

This course focuses on understanding machine learning tools and techniques, including the terminology behind machine learning, when to use machine learning, the theory behind the various learning approaches, and their effective use to solve real-world problems. It focuses on machine learning techniques, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. Students perform hands-on activities using various machine learning models, closely examining the data requirements and algorithms to ensure ethical use and effectiveness. The course uses case studies and existing data sets to examine how machine learning is used in the real-world, including making predictions and artificial intelligence with government data, business information, and in science, biomedicine, and cybersecurity. Students must achieve a minimum grade of C. Prerequisite: DATA 325 with a grade of C or higher.