Applied Machine Learning

3 credits · Undergraduate

This course explores key areas of machine learning and data mining, focusing on the best ways to apply these concepts in actual systems. This course focuses on supervised machine learning methods, but it also touches on unsupervised learning. Key subjects include essential algorithms like linear and logistic regression, decision trees, support vector machines, and clustering, along with neural networks. Students also delve into important methods for choosing the right features in data, reducing data complexity, estimating errors, and practical testing of these methods. Prerequisite(s): CPSC 371-3 or permission of the instructor