The introduction to statistics and data processing
Optimization methods: unconstrained problems, problems with equality and inequality constraints
Data processing software: R, Matlab, Excel, Python
Regression models (linear, polynomial, non-linear, autoregression), regularization
Multicriterial optimization
Bayesian inference methods, Markov chains
Spectral analysis: Principal Component Analysis, eigenvalue and singular decompositions
Clustering: K-means, spectral clustering
Time-series: introduction, graphical analysis, descriptive analysis, measures of dynamism
Model suitability analysis, introduction to theory of information
Optimization methods: unconstrained problems, problems with equality and inequality constraints
Data processing software: R, Matlab, Excel, Python
Regression models (linear, polynomial, non-linear, autoregression), regularization
Multicriterial optimization
Bayesian inference methods, Markov chains
Spectral analysis: Principal Component Analysis, eigenvalue and singular decompositions
Clustering: K-means, spectral clustering
Time-series: introduction, graphical analysis, descriptive analysis, measures of dynamism
Model suitability analysis, introduction to theory of information