The University of Texas at Austin 3 credits Aerospace Engineering
Examine basics of vectors and matrices, linear equations, regression and classification, similarity measures, the Discrete Fourier Transform (DFT), linear filters, and power spectrum estimates. Focus on applying matrix methods to practical applications, such as tomography, image processing, data fitting, time series prediction, optimal control, finance, and machine learning. Utilize MATLAB or Python to do computations with vectors and matrices and run numerical experiments with real-world data sets. Aerospace Engineering 389P (Topic 16) and 389 (Topic: Computational Methods) may not both be counted.
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