- Schedule of Classes - May 20, 2019 7:14PM EDT
- Course Catalog - May 20, 2019 7:15PM EDT
Course information provided by the Courses of Study 2018-2019.
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: Basic probability (CEE 3040/MATH 4710/ORIE 3500 or equivalent) and optimization (CHEME 6800/SYSEN 6800, ORIE 3310/ORIE 5310, or ORIE 5380).
Comments Co-meets with SYSEN 5880.
Credits and Grading Basis
4 Credits GradeNoAud(Letter grades only (no audit))
Class Number & Section Details
- MWFrank H T Rhodes Hall 253
Enrollment limited to PhD& MS students.
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