Artificial Intelligence


Type of course: Lectures/consultations + practice
Number of credits: 4
Hours: (Lecture/Seminar/Laboratory): 30+15+0

Host institute: Institute of Basic and Technical Sciences

Purpose and requirements of the subject:
The basic purpose of this subject is to give an overview of the typical methods of artificial intelligence and their application. After completing the subject students are able to analyse the specialities of the tasks in their job with an approach of applying artificial intelligence methods and tools. They are able to outline the significant and essential features of an actual task and discuss it with an artificial intelligence expert. They are also able to use certain tools in computer modelling.
Description of the subject:
main topics of the subject:

  • Introduction and overview (Different definitions of artificial intelligence. The concept of the rational agent. The agent and its environment. Main agent types. Main problems of artificial intelligence.)
  • Task solution with search procedures: how to define a search problem. Basic types of search problems. Examples. Search on trees and graphs. General search algorithms. Search strategy features. Blind search methods. Iterative search. Heuristic search. Genetic and evolution algorithms.)
  • Knowledge representation and conclusion (Knowledge and representation. Syntax, semantics, interpretation. Judgement calculation and first-order logic. Conclusion rules, correct and complete conclusion. Conclusion methods, resolution. Basic methods for managing insecure knowledge – modelling on Bayes theorem, fuzzy logic.)
  • Learning methods (Basic issues of concept description and learning. Inductive and deductive learning. Concept learning decision trees, condition spaces. Non-symbolic methods: neural networks. Basic elements, operational method, learning. Controlled and uncontrolled procedures.)
  • Physical agents - robots (The concept, structure and application of robots. Intelligent robots.)
  • Artificial intelligence application in production (Controlling production machines, planning, supervising and optimizing the production process. Intelligent production systems.)

Teaching method:

  • Lectures, consultations, practice
  • Consultation with tutors

Coursebooks, reference material:
electronic reference material and lecture outlines on ILIAS electronic learning management surface

Linear Algebra (obligatory)
written examination assessed on a 5-graded scale