450-4049/02 – 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 | | Semester | winter |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | 2021/2022 |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Students will learn about the basics of the science of artificial intelligence, learn about its tools in engineering application areas with respect to the teaching of the subject in the given study program. They will learn about the methods of synthesis of simple artificial intelligence systems. Students will be able to make practical use of artificial intelligence tools, design a fuzzy expert system, an artificial neural network or an optimization algorithm with respect to applications in their field of study.
Teaching methods
Lectures
Tutorials
Experimental work in labs
Summary
The course is primarily focused on gaining knowledge of the function and potential application of artificial intelligence methods in the context of their programme of study. The course will introduce students to selected artificial intelligence methods and focus on their practical implementation in their engineering practice. Core areas include fuzzy logic and expert systems, cluster analysis and optimization methods, neural networks, decision trees and forests, machine learning methods without neural networks, hybrid and special machine learning methods, and the use of generative artificial intelligence.
Compulsory literature:
Recommended literature:
Additional study materials
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
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 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
Assessment of instruction
Předmět neobsahuje žádné hodnocení.