450-4049/03 – Applied Artificial Intelligence Methods (AUI)
| Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
| Subject guarantor | prof. Ing. Martin Černý, Ph.D. | Subject version guarantor | prof. Ing. Martin Černý, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 2 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2019/2020 | 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:
Additional study materials
Way of continuous check of knowledge in the course of semester
Control during semestr is on the basis of student participation in laboratory exercises.
Conditions for awarding a classified credit:
The student can achieve 40 points for protocols from laboratory exercises and a test of practical skills. The minimum number of points to be awarded is 20.
To complete the course the student has to pass a written test focused on theoretical knowledge with a minimum of 30 points out of 60 possible.
E-learning
Other requirements
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 to the artificial intelligence.
2. Principles and methods of the artificial intelligence. Methods of computer representation, knowledge, and linguistic modeling.
3. Basic of the fuzzy mathematics, and fuzzy logic.
4. Fuzzy expert systems.
5. Fuzzy models.
6. Data classification: basic methods, principles, and applications in the biomedicine.
7. Neural networks: basic principles, topologies, types of the neural networks, and applications for the classification and prediction of the biomedical image data.
8. Basic methods and applications of the optimization methods for processing of the biomedical image data.
9. Genetic and evolutionary algorithms for solving the complex optimization problems.
10. Hierarchical and non-hierarchical methods of the clustering analysis.
Computer Exercises:
1. Introduction to mathematical modeling in the SW MATLAB.
2. Functionalities of the artificial intelligence in the SW MATLAB.
3. Mathematical applications of the fuzzy mathematics.
4. Design and realization of the fuzzy expert systems.
5. Application of the fuzzy modeling on real biomedical examples.
6. Implementation of selected classification algorithms in a context of the biomedical applications.
7. Design and realization of the neural networks in the MATLAB for solving the classification and prediction tasks.
8. Application of the optimization techniques for solving the complex mathematical issues.
9. Implementation of the selected genetic algorithms in an area of the biomedical signal and image processing.
10. Implementation of the clustering analysis methods for segmentation and classification of the biomedical data.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction