Course detail
Multi-valued Logic Applications
FSI-SAL Acad. year: 2023/2024 Summer semester
The course is designed for students of mathematical engineering and contains the theory of fuzzy logic, linguistic variables and linguistic models and the theory of expert systems. The subject also includes the practical design of an expert system based on Lukasiewicz or Mamdani logic.
The second part of the course is devoted to machine learning and neural networks, which are used for modern applications of expert systems. Students become familiar with basic terminology, other types and their use for applications (speech, image, etc. analysis).
Language of instruction
Czech
Number of ECTS credits
4
Supervisor
Department
Entry knowledge
Mathematical logic, Fuzzy set theory.
Rules for evaluation and completion of the course
Graded assessment based on submission of semester work (70 percent) and oral exam of the given theory (30 percent).
Participation on lessons is compulsory, in case of absence it is necessary to work out substitute work.
Aims
The aim of the course is to introduce the methods of fuzzy logic and the proposition of expert systems. Next, students will learn to design a simple system based on machine learning and will learn the theoretical and practical basics of neural networks.
1.Terminology and explanation of the concepts of many-valued logic.
2. ntroducing word models, designing an expert system.
3. Machine learning methods.
4. Neural networks (NN) – basic properties and concepts.
5. Use of NN for analysis of text, speech, image (CNN). Design your own neural network without even using pre-trained models.
The study programmes with the given course
Programme N-MAI-P: Mathematical Engineering, Master's, elective
Type of course unit
Lecture
26 hours, compulsory
Syllabus
1. Multi-valued logic, formulae.
2. T-norms, T-conorms, generalized implications.
3. Linguistic variables and linguistic models, knowledge bases of expert systems.
4. Lukasiewicz logic, Mamdani principle.
5. Inference techniques and its implementation, redundance and contradictions in knowledge bases, Fuzzification and defuzzification problem.
6. Expert system project.
7.Machine learning (decisiton trees, kNN, SVM).
8.-9. Text mining, chatbot
10. Neural network elementary principles, Deep Learning.
11. Convolutional Neural Network (CNN).
12.-13. Semestral work, consultation.
Computer-assisted exercise
13 hours, compulsory
Syllabus
Topics for work in exercises are closely related to the lectures. As part of the computer exercises, particular areas will be implemented in Matlab software, event. Python. IBM Watson Assistant will be used to design the chatbot.
1. Multi-valued logic, formulae.
2. T-norms, T-conorms, generalized implications.
3. Linguistic variables and linguistic models, knowledge bases of expert systems.
4. Lukasiewicz logic, Mamdani principle.
5. Inference techniques and its implementation, redundance and contradictions in knowledge bases, Fuzzification and defuzzification problem.
6. Expert system project.
7.Machine learning (decisiton trees, kNN, SVM).
8.-9. Text mining, chatbot
10. Neural network elementary principles, Deep Learning.
11. Convolutional Neural Network (CNN).
12.-13. Semestral work, consultation.