Intelligent methods in biomedicine data-mining

Higher education teachers: Škrjanc Igor

Subject description

Prerequisites:

• Completed undergraduate studyprogramme in the field of electricalengineering or related engineering ornatural and mathematical sciences.
• Enrolment in the 2nd year of the Master’sstudy programme for Electrical Engineering(2nd cycle).
• Basic knowledge of applied mathematics(vectors and matrices, eigenvectors andeigenvalues, some linear algebra)

Content (Syllabus outline):

• 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.

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.

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.

Learning and teaching methods:

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

Study materials

1. I. Škrjanc: Inteligentni sistemi pri raziskovanju podatkov in odločanju, skripta v pripravi