Course detail
Artificial Intelligence Algorithms
FSI-VAI Acad. year: 2024/2025 Winter semester
The course introduces basic approaches to artificial intelligence algorithms and classical methods used in the field. Main emphasis is given to automated formulas proves, knowledge representation and problem solving. Practical use of the methods is demonstrated on solving simple engineering problems.
Language of instruction
Czech
Number of ECTS credits
4
Supervisor
Department
Entry knowledge
Knowledge of algorithmization, programming and the basics of mathematical logic and probability theory are assumed.
Rules for evaluation and completion of the course
Course-unit credit requirements: Creation of functional software projects using some of the discussed AI methods and working out a presentation of some undiscussed AI method. Student can obtain 100 marks, 40 marks during seminars (30 for projects and 10 for the presentation; he needs at least 20), 60 marks during exam (he needs at least 30).
The attendance at lectures is recommended, at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a teacher.
Aims
Knowledge of the basic means of artificial intelligence and the possibilities of their use in solving engineering tasks.
Understanding of basic methods of artificial intelligence and ability of their implementation.
The study programmes with the given course
Programme N-AIŘ-P: Applied Computer Science and Control, Master's, compulsory
Programme N-MAI-P: Mathematical Engineering, Master's, compulsory-optional
Programme C-AKR-P: , Lifelong learning
specialization CZS: , elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
1. Introduction to artificial intelligence.
2. State space, uninformed search.
3. Informed search in state space.
4. Problem solving by decomposition into sub-problems, AND/OR search methods.
5. Game playing methods.
6. Constraint satisfaction problems.
7. Predicate logic and resolution method.
8. Horn logic and logic programming.
9. Representation, use and learning of knowledge.
10. Representation and processing of uncertainty.
11. Bayesian and decision networks.
12. Non-traditional logics.
13. Markov decision processes.
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
1. Introductory motivational examples.
2. Uninformed methods of state space search.
3. Informed methods of state space search.
4. A* algorithm and its modifications.
5. Methods of AND/OR graph search.
6. Game playing methods.
7. Constraint satisfaction problems.
8. Predicate logic and resolution method.
9. Logic programming and Prolog.
10. Solving AI problems in Prolog.
11. Learning symbolic knowledge.
12. Bayesian networks.
13. Probabilistic and fuzzy logic programming.