545-0907/01 – Applied Artificial Intelligence (AUI)
| Gurantor department | Department of Economics and Control Systems | Credits | 10 |
| Subject guarantor | doc. Ing. Pavel Staša, Ph.D. | Subject version guarantor | doc. Ing. Pavel Staša, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory |
| Year | | Semester | winter + summer |
| | Study language | Czech |
| Year of introduction | 2008/2009 | Year of cancellation | 2020/2021 |
| Intended for the faculties | HGF, FS, FMT | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
Subject Applied Artificial Intelligence (AAI) is designed according to the specific focus on the student's selected area of artificial intelligence. The student acquires the knowledge and skills to analyze technical task in terms of applications selected disciplines AAI, synthesize and verify the application in laboratory conditions, especially with the use of simulation and modeling. Results of laboratory experiments and skills acquired by students are used in the processing of the dissertation.
Teaching methods
Lectures
Individual consultations
Project work
Summary
Subject describes applications of artificial inteligence in industry and cybernetics systems. Key topics are knowledge processing, knowledge systems, expert systems applications, semantic nets and frames, data mining and fuzzy systems application, artificial neural networks, machine learnig and learnig systems, image processing and computer graphics, evolutiona and genetic algorithms, artificial life, biocybernetics.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
1. Zadání samostatné práce v souvislosti s tématem disertační práce.
2. Konzultace zvoleného řešení a jeho úpravy.
3. Předložení odborné práce v rozsahu cca 20 stran.
4. Obhájení odborné práce při zkoušce z předmětu.
E-learning
Other requirements
Individuální, dle specifikace vedoucího cvičení.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Course Artificial Intelligence (AI), the definition, classification.
2. Fuzzy logic (FL), fuzzification, defuzzyfication, inference rules, fuzzy controllers.
3. Evolution algorithms (EA). Method of differential evolution (DE), self-organizing migration algorithm (SOMA). Genetic algorithms (GA), inheritance, crossover, mutation, selection, fitness function.
4. Artificial Neural Networks (ANN), division, operating principles, characteristics, processing of uncertain information in a neural network, learning in neural networks.
5. Multi-Agent Systems (MAS), distributed artificial intelligence, the concept of agent, distribution agent, adaptability, communication.
6. Expert systems, principles, knowledge base, knowledge-based systems.
7. Software tools used in the implementation of the AI.
8. Solving of problems of interpretation, diagnosis, prediction.
9. Task planning and recognition.
10. Control and optimization solving with using AI.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.