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|CS 2810 - Mathematics of Data Models|
Studies the methods and ideas in linear algebra, multivariable calculus, and statistics that are most relevant for the practicing computer scientist doing machine learning, modeling, or hypothesis testing with data. Covers least squares regression, finding eigenvalues to predict a linear system’s behavior, performing gradient descent to fit a model to data, and performing t-tests and chi-square tests to determine whether differences between populations are significant. Includes applications to popular machine-learning methods, including Bayesian models and neural networks.
4.000 Credit hours
4.000 Lecture hours
Schedule Types: Lecture
Computer Science Department
NUpath Analyzing/Using Data, NUpath Formal/Quant Reasoning, Computer&Info Sci