The University of Texas at Austin 1-4 credit Computational Engineering
Focus on algorithms, mathematics, implementations (on Scikit-learn), and real-life applications of machine learning and data sciences including machine learning algorithms such as supervised learnings (linear models, support vector machines, kernel methods, tree-based algorithms), unsupervised learnings (Density estimation, clustering, dimensionality reduction, PCA), and deep neural networks. Explore the foundations of machine learning including optimization, probability, advanced linear algebra, statistics, and methods to evaluate machine learning methods. Computational Engineering 379L (Topic: Intro to Machine Learning) and 379L (Topic 1) may not both be counted. Pre/Corequisites: Computational Engineering 322, and Mathematics 427J or 340L with a grade of at least C- in each. Also offered as 279L.1, 379L.1, 479L.1 for different credit.
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