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Fall 2014: Applied matrix algorithms for signal processing, data analysis and machine learning

Course No.: 453
Credit Hours: 4
Instructor: Raj Nadakuditi
Prerequisites: EECS 301 or MATH 425 or STATS 215 or STATS 412 or STATS 426 or IOE 265 or equivalent

Course Description:
Theory and application of matrix algorithms 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. Greater emphasis on applications than in EECS 551.
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