Course: Introduction to Artificial Intelligence

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Course title Introduction to Artificial Intelligence
Course code KIT/ZNUI1
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction English
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Pozdílek Martin, Ing. Ph.D.
Course content
1. Introduction (Introduction to AI, basic terms, AI segmentation). 2. Graph theory basics, planning. 3. Transition system, problem formulation, uninformed state-space search (depth first search, breadth first Search). 4. Uninformed state-space search (limited depth first search, iterative depth first search, lowest cost first Search). 5. Informed state-space search, heuristic function. 6. Games theory, minimax algorithms, minimax algorithm with alfa-beta prunning, heuristic minimax. 7. Fuzzy sets, basic operations. 8. Fuzzy relations and operations with fuzzy relations, fuzzy numbers. 9. Linguistic variable, fuzzy logic. 10. Fuzzy logic systems. 11. Knowledge, knowledge representations, expert systems 12. Expert systems. 13. Repetition.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Methods of individual activities
  • Individual project - 45 hours per semester
  • Contact teaching - 65 hours per semester
  • Home preparation for classes - 26 hours per semester
  • Preparation for an exam - 14 hours per semester
Learning outcomes
The main aim of the course is to familiarize students with the basics and the structure of the artificial intelligence scientific discipline. In additition, students will be provided with knowledge in problem solving, games theory, planning, fuzzy logic and expert systems.
Basic orientation in the artificial inteligence problems. Ability to use optimization methods, problém solving techniques, fuzzy systems building and orientation in expert system problems.
Prerequisites
There is expected fundamental knowledge of programming and graph theory.

Assessment methods and criteria
Oral examination, Home assignment evaluation

Solving of the final complex project comprising of different tasks from individual parts of the subject.
Recommended literature
  • NEGNEVITSKY M. Artificial Intelligence : A Guide to Intelligent Systems (2nd Edition).. Addison Wesley, 2004. ISBN 0321204662.
  • Russell, Stuart J. Artificial intelligence : a modern approach. Harlow: Pearson Education, 2014. ISBN 978-1-292-02420-2.
  • ŠKRABÁNEK, P. Základy umělé inteligence (elektronické učební texty. 2010.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester