Methods and models for multivariate data analysis

Entry requirements: Basic knowledge in field of probability theory and mathematical statistics.

Credits: 4

Semester: 2

Course: Core


Students will improve backgrounds in probability theory; develop skills in probabilistic modelling and statistic assessment.


Main topics of the discipline:
-Probabilistic models for random variables. Univariate random variable. CDF and PDF. Probability distribution parameters estimation. Probabilistic interval. Confidence interval. Tolerant interval.
- Probabilistic models for random variables. Multivariate random variable. Regression and correlation analysis. Linear and non-linear regression. Canonical correlations. Principal component analysis. Empirical orthogonal functions. Factor analysis. ANOVA method. Cluster analysis. Multidimentional interval estimates. Nonlinear methods for dimensionality reduction.


Lectures, seminars, practical classes.


60% results of practical tasks, 30% participation in class discussions and/or individual presentation on a topic of interest, 10% results of tests.
Additional opportunity to improve scores during the exam.