Foundations of Machine Learning

3 credits · Undergraduate

Also listed as ECE 4262. Theoretical and algorithmic foundations of supervised learning, unsupervised learning, and reinforcement learning. Linear and nonlinear regression, kernel methods, support vector machines, neural networks and deep learning methods, instance-based methods, ensemble classifiers, clustering and dimensionality reduction, value and policy iteration. Explainable AI, ethics, and data privacy. Serves as repeat credit for ECE 4262. No credit for students who have earned credit for CS 5262 or ECE 5262. Prerequisite: CS 3251 or ECE 3251; one of MATH 2810, 2820, or 3640; one of MATH 2410, 2500, 2501, or 2600. SPRING. [3]