Foundations of Machine Learning

3 credits · Graduate School

Also listed as ECE 5262. 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 5262. No credit for students who have earned credit for CS 4262 or ECE 4262. SPRING. [3]