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Subject offerings of new and developing areas of knowledge in agricultural and biological engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary to a maximum of 16 hours.

Title: Applied Data Science and Machine Learning for Agricultural and Biological Engineering In this 3-credit course, students will learn to leverage data to study agricultural and biological engineering problems, identify patterns, and make actionable insights. This course includes training in (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. Prerequisite: MATH 257; CS 101.