Department of Applied Mathematics
Basic principles of traffic detection and data collection, specific problems of the field of traffic data. Data preprocessing and analysis for use in additional applications.
Department of Applied Mathematics
Mathematical modeling. The system and its mathematical description. Types of signals. Basic system responses. Convolution. State models. Principle of general / stationary / linear state description. Data measurement. Uncertainty in measured data. Data normalization. Preparation of data for further processing. Linear state model over noisy data. Kalman filter condition estimation. Statistical learning methods. Regression, classification.
Department of Applied Mathematics
System. Regression, discrete and logistic models. Bayesian estimation of model parameters. Parameter estimation of normal regression, discrete and logistic models. Classification with logistic model. One-step and multi-step prediction with regression and discrete models. State model. State estimation. Kalman filter. Control with regression and discrete models.
Department of Applied Mathematics
Introduction to smart cities, systém analysis and design fundamentals, usage of UML for system design, principles of complex systems, modeling using multiagent systems in the SW environment AnyLogic, application on a small scale real world problem.
Department of Applied Mathematics
Basics of probability Descriptive statistics Population and sample, limit theorem Point estimate, construction and properties Interval estimates Parametric tests Nonparametric tests Regression and correlation analysis
Department of Applied Mathematics
The subject deals with the problems of mathematical modelling of dynamical systems, estimation od these models and their utilization for prediction. The results are illustrated on practical transportation tasks. Mathematical theory roots from probability and mathematical statistics and they use the methods of the Bayesian probabilistic approach.