Intro to Machine Learning

Catalog description

Machine learning is a branch of artificial intelligence rooted in computational statistics which focuses on the development of models and algorithms capable of identifying patterns in data. Throughout the course students will use machine learning methods to solve problems from a variety of disciplines. Topics include model validation and optimization, boosting and bagging algorithms, neural networks, transfer learning, and related topics. Students will complete a semester-long capstone project. Prerequisite: MAT-215 and STA-270.