450-4049/02 – Applied Artificial Intelligence Methods (AUI)
| Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
| Subject guarantor | doc. Ing. Martin Černý, Ph.D. | Subject version guarantor | doc. Ing. Martin Černý, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | | Semester | winter |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The subject represents the introduction to the principles of scientific field of artificial intelligence. The goal of subject is introduce students on analysis and design of artificial intelligence tolls in the field of biomedical engineering.
Students will be ready for practical use of basic artificial intelligence tools namely fuzzy expert systems, artificial neural networks and genetic algorithms in the field of BME.
Teaching methods
Lectures
Tutorials
Experimental work in labs
Summary
Subject deals with gathering of knowledge and applications of the artificial intelligence methods in the context of processing and modeling of the biomedical image data. Subject is composed from four main areas of the artificial intelligence. The first part of the subject deals with the fuzzy mathematics, fuzzy modeling, and design of the expert systems. The second part of the subject deals with the data classification with emphasis to an area of the neural network. Next area deals with optimization techniques with emphasis of an analysis of the genetic algorithms for solving of the complex mathematical problems. The last part of the subject focuses to hierarchical and non-hierarchical methods of the cluster analysis.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Verification of study:
The current activity of student is done through his laboratories activities.
Conditions for graded credit:
Student can gain up to 40 points from the laboratory excercices. To pass the laboratory part of the course student has to gain at least 21 points
To pass the course student has to pass both of the laboratory part of the course and the final written proof 60, min 30 points.
E-learning
Další požadavky na studenta
There are not defined other requirements for students
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
1. Introduction on artificial intelligence scientific field
2. Principles of artificial intelligence, importace of knowledge in problem solving tasks
3. Methods of computer knowledge reprezentation
4. Mathematic and linguistc modelling
5. Vagueness formalization of knowledge in linguistic models
6. Principles of fuzzy set and fuzy logic theory
7. Fuzzy models of Mandami type
8. Fuzzy models of Takagi-Sugeno type
9. Diagnostoc expert systems
10. Fuzzy controllers
11. Topology and functions of multilayer artificial neural networks
12. Neural networks application in biomedical engineering
13. Genetic algorithms - versatil optimization methods
14. Genetic algorithms application in adaptation procedures
Laboratories:
1. Fuzzy controller using microcomputer and PLC
Computer labs:
1. Computer system MATLAB
2. Fuzzy ToolBox in MATLAB
3. Fuzzy sets and vagueness objects reprezentation in MATLAB
4. Fuzzy conditonal rules formalization in MATLAB
5. Fuzzy modelling of Mandami type in MATLAB
6. Fuzzy modelling of Takagi-Sugeno type in MATLAB
7. Diagnostic fuzzy expert systems
8. Fuzzy controllers in MATLAB
9. Pletysmogram evaluation fuzzy expert module
10. EEG evaluation fuzzy expert module
11. Neural network synthesi in Neural ToolBoxu of MATLABu
12. Neural network application in biomedical engineering
13. Genetic algorithm synthesis in MATLAB
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks