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
SectionSessionTypeTimeDaysRegisteredInstructorLocationSyllabusInfo
29545D048Lecture1:00-3:50pmFriday23 of 50Joe QinZHS163feesession dates
Information accurate as of October 9, 2019 8:14 am.