ECE 3702 Applied Machine Learning Engineering 3.0 Credits
This course emphasizes how to gather data then train, test, and deploy practical machine learning systems using modern software libraries, with an emphasis on scikit-learn, Keras, pytorch, and agents. After garnering working familiarity with learning architectures including linear regression, support vector machines, decision trees, ensemble methods, convolutional neural networks, and transformers, students shift to practicing techniques that leverage state of the art published models via transfer learning. This is a hands-on project-focused course integrating coding activities into lectures. To provide the broadest applicability, datasets will range from rich text, to financial time series, to sound, images, and video, as well as data garnered through game play.
Repeat Status: Not repeatable for credit
Prerequisites: ECE 1210 [Min Grade: D] or ECE 105 [Min Grade: D]