ABE 412

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Credit: 3 hours.

Use of data to study problems unique to agricultural and biological engineering, identify patterns, and make actionable insights. Course includes (1) exploratory data analysis including data profiling, missing data, description, and data visualization; (2) data processing techniques including singular value decomposition, dimensionality reduction, and Fourier and wavelet transforms; (3) machine learning techniques including regression, classification, feature selection, clustering, and neural networks.

3 undergraduate hours. 3 graduate hours. Prerequisite: CS101 or equivalent and MATH 257.

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