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Sep 07, 2024
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USC Catalogue 2022-2023 [ARCHIVED CATALOGUE]
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CHE 586 Process Data Analytics and Machine Learning Units: 3 Terms Offered: Sp Topics include multi-linear regression, supervised learning, unsupervised learning, principal component analysis, partial least squares, canonical correlation analysis, clustering methods, lasso, neural networks, and deep learning. Applications include analysis of chemical process data, quality data, and indirectly measured data. Recommended Preparation: Programming experience in R or MATLAB preferred; an understanding of engineering statistics and knowledge of matrix operations; emphasis is on linear algebraic approach over a probabilistic approach Registration Restriction: Open only to graduate students Instruction Mode: Lecture Grading Option: Letter
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