• Provides an introduction to data analytics for manufacturing processes. Topics include chemometrics, discriminant analysis, hyperspectral imaging, machine learning, big data, Bayesian methods, experimental design, feature spaces, and pattern recognition as relevant to manufacturing process applications (e.g., output estimation, process control, and fault detection, identification and diagnosis). Students taking graduate version complete additional assignments.

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