Course detail
Artificial Intelligence Algorithms
FSI-VAI-A Acad. year: 2024/2025 Winter semester
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
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 tutor.
Language of instruction
English
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.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme N-MAI-A: Mathematical Engineering, Master's
branch ---: no specialisation, 4 credits, compulsory-optional
Type of course unit
Lecture
26 hours, optionally
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
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.