Computer Science
CS 2810: Mathematics of Data Models
Lecture - 4 credits
ND
EI
IC
FQ
SI
AD
DD
ER
WF
WD
WI
EX
CE
- 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.
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. Show more.