Intelligent methods in biomedicine data-mining

Course description

  • 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.

Course is carried out on study programme

Elektrotehnika 2. stopnja

Objectives and competences

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.

Learning and teaching methods

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

Intended learning outcomes

Knowledge and understanding:

After completing this course the student will be able to demonstrate a knowledge and understanding of the:

  • construction of intelligent systems which are basis to understand and analyse biomedicine systems,
  • data-mining of biomedicine data based on intelligent methods.

The use of knowledge:

The student will be able to use the acquired knowledge to construct different intelligent systems for data-mining and monitoring of biomedicine data. The student will be able to critically evaluate the consistency between the acquired knowledge and the application of the concepts in practice.

Transferable skills:

  • the use of literature and other resources in the fields of intelligent systems in data mining and system monitoring;
  • the use of development tools and environments for computer programming (writing computer programs in different programming languages or using the Matlab development environment);
  • problem solving: problem analysis, algorithm design, implementation and testing of a program.

Reference nosilca

  1. ŠKRJANC, Igor. Evolving fuzzy-model-based design of experiments with supervised hierarchical clustering. IEEE transactions on fuzzy systems, ISSN 1063-6706. [Print ed.], 2014, vol. , no. , str. 1-12.
  2. ŠKRJANC, Igor. Fuzzy confidence interval for pH titration curve. Applied mathematical modelling, ISSN 0307-904X. [Print ed.], Aug. 2011, vol. 35, no. 8, str. 4083-4090.
  3. HARTMANN, Benjamin, BÄNFER, Oliver, NELLES, Oliver, SODJA, Anton, TESLIĆ, Luka, ŠKRJANC, Igor. Supervised hierarchical clustering in fuzzy model identification. IEEE transactions on fuzzy systems, ISSN 1063-6706. [Print ed.], Dec. 2011, vol. 19, no. 6, str. 1163-1176.
  4. BELIČ, Aleš, ŠKRJANC, Igor, ZUPANČIČ-BOŽIČ, Damjana, VREČER, Franc. Tableting process optimisation with the application of fuzzy models. International journal of pharmaceutics, ISSN 0378-5173. [Print ed.], Apr. 2010, vol. 389, no. 1/2, str. 86-93.
  5. ŠKRJANC, Igor. Confidence interval of fuzzy models : an example using a waste-water treatment plant. Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439. [Print ed.], Apr. 2009, vol. 96, no. 2, str. 182-187.

Study materials

I. Škrjanc: Inteligentni sistemi pri raziskovanju podatkov in odločanju, skripta, 2016.

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E:  dekanat@fe.uni-lj.si T:  01 4768 411