Courses Detail Information
ECE6609J – Matrix Methods for Signal Processing, Data Analysis and Machine Learning
Instructor: Yong Long
Credits: 3 credits
Pre-requisites: VE351(Digital Signal Processing) or graduate standing.
Theory and application of matrix methods to signal processing, data analysis and machine learning. Theoretical topics include subspaces, eigenvalue and singular value decomposition, projection theorem, constrained, regularized and unconstrained least squares techniques and iterative algorithms. Applications such as image deblurring, ranking of webpages, image segmentation and compression, social networks, circuit analysis, recommender systems and handwritten digit recognition.