Mathematical modeling, state model, statistical learning, regression, classification, regularization, clustering, single- and multicriteria optimization.
Abstract:
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.
Objectives:
The course deals with the theoretical foundations and applications of mathematical algorithms and numerical procedures used in the field of intelligent transport systems. It functions as a basis for follow-up professional subjects in the field of ITS.