- Schedule of Classes - June 25, 2020 7:14PM EDT
- Course Catalog - June 25, 2020 7:15PM EDT
Course information provided by the Courses of Study 2019-2020.
This course covers the basic concepts, models and algorithms of Bayesian learning, classification, regression, dimension reduction, clustering, density estimation, artificial neural networks, deep learning, and reinforcement learning. Application and methodology topics include process monitoring, fault diagnosis, preventive maintenance, root cause analysis, soft sensing, quality control, machine learning for process optimization, data-driven decision making under uncertainty, missing data imputation, data de-noising, and anomaly/outlier detection.
When Offered Spring.
Prerequisites/Corequisites Prerequisite: CEE 3040 or MATH 4710 or ORIE 3500 or equivalent, CHEME 6800/SYSEN 6800 or ORIE 3310 or ORIE 5310 or ORIE 5380.
Regular Academic Session. Combined with: CHEME 6880
Credits and Grading Basis
4 Credits Opt NoAud(Letter or S/U grades (no audit))
Class Number & Section Details
- MWFrank H T Rhodes Hall 253
- Jan 21 - May 5, 2020
Instruction Mode: Hybrid - Online & In Person
Enrollment limited to PhD& MS students.
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