Chemical Engineering 586:
Process Data Analytics and Machine Learning (3.0 units)
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.
- Restriction: Registration open to the following class level(s): Doctoral Student, Master Student
|29545D||048||Lecture||1:00-3:50pm||Friday||23 of 50||Joe Qin||ZHS163|