ECE 657Introduction to Machine Learning

This course provides a rigorous examination of Machine Learning (ML), structured to progress from fundamental principles to advanced methodologies. It encompasses a broad spectrum of supervised learning topics, including nonparametric and parametric models, linear and nonlinear approaches, deep learning paradigms, and probabilistic models. In the domain of unsupervised learning, students will delve into clustering, density estimation, and dimensionality reduction, while reinforcement learning will be introduced preliminarily.