DSC 395 Machine Learning
An in‐depth treatment of machine learning. Topics may include supervised and unsupervised approaches to learning, assessing and comparing algorithms, classification, regression, clustering, parameter estimation, generative methods, reinforcement learning, dimensionality reduction, structured prediction, anomaly detection, ensemble learning methods, and significant applications. Students will gain an understanding of the mathematical foundations of each method and will learn to implement, evaluate, and apply several of those methods.
Prerequisite
DSC 305, CSC 270, MAT 240, and MAT 310 or permission of the instructor.