DSC 301 - Introduction to Machine Learning

Introduction to machine learning, including necessary linear algebra topics, such as vectors, matrices, eigenvalues, eigenvectors, symmetric matrices and singular value decompositions. Considers gradient descent methods to optimize predictive and classification models, dimension reduction, clustering, cross validation, grid search, data visualization and dashboards.

Credit Hours: 3

Prerequisite Courses: MAT 151 or MAT 161.
Course Repeatability: Course may not be repeated. Maximum Repeatable Hours: 3

Click here for the Spring 2025 Class Schedule.