Intelligent methods in biomedicine data-mining

• Introduction to intelligent systems. Intelligent systems in data-mining, classification in biomedicine, control and fault detection.
• Basic methods of local nonlinear optimization used in intelligent systems and global nonlinear optimization methods.
• Methods of global nonlinear optimization: simulated annealing, evolutionary algorithms, particle swarm optimization, genetic algorithms, branch and bound algorithms.
• Unsupervised learning methods. Principle component analysis.  PCA in identification, data filtering, data compression and fault detection.
• Data clustering. Methods of clustering: fuzzy c-means, Gustafon-Kessel fuzzy c-means, possibilistic c-means clustering, method of regression clustering.
• Optimization of complex models. Verification and validation of models. Explicit and implicit optimization of model structure.
• Static models. Model based on basis function formulation. Polynomial models.
• Neural networks. Multilayer perceptron network. Radial basis function networks in function approximation. Examples from biomedicine.
• Fuzzy and neuro-fuzzy models. Fuzzy logic. Types of fuzzy models. Estimation of fuzzy model parameter. Global and local estimation.
• Expert systems based on fuzzy models. Development of expert systems. Examples of expert systems in biomedicine.
• Nonlinear dynamical systems. Classical polynomial models in nonlinear modelling. Dynamical fuzzy and neuro-fuzzy modelas.

To provide students with an understanding of the basic mathematical and computational principles of constructing artificial perception systems, which are an essential part of intelligent systems in automation and control.

• lectures,
• laboratory exercises and projects,
• coursework.