Mar 28, 2024  
USC Catalogue 2020-2021 
    
USC Catalogue 2020-2021 [ARCHIVED CATALOGUE]

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