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Fall 2019 Semester
Sep 22, 2021
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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

Levels: Undergraduate
Schedule Types: Lecture

Computer Science Department

Course Attributes:
NUpath Analyzing/Using Data, NUpath Formal/Quant Reasoning, Computer&Info Sci

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