The University of Texas at Austin 1-4 credit Computational 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. Computational Engineering 379L (Topic: Computational Methods) and 379L (Topic 3) may not both be counted. Pre/Corequisites: Mathematics 408D, 408L, or 408M and Computational Engineering 301 with a grade of at least C- in each. Also offered as 279L.3, 379L.3, 479L.3 for different credit.
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