Probabilistic inference for data science 1Laajuus (5 cr)
Course unit code: C-10088-3622352
General information
- Credits
- 5 cr
- Teaching language
- English
- Finnish
- Institution
- University of Eastern Finland
Objective
Learning outcomes: Goal of the course is to learn basics of probability calculus for data science, especially mathematical formulation of univariate and multivariate random variables and the required matrix calculus.
Content
The course covers the essential probability calculus for data science. Univariate distributions, transformations and their distribution, expected value, variance and standard deviation. Bivariate distributions, conditional distributions, marginal distributions, covariance, correlation and independence. Matrix calculus. Multivariate distribution, its expected value, variance-covariance matrix and conditional and marginal distributions. Multivariate normal distribution and its essential properties
Qualifications
Introduction to statistics, recommended R-course or ability to use some other software for numerical computation.