Faculty of Information Technology, BUT

Course details

Intelligent Systems

SIN Acad. year 2006/2007 Winter semester 5 credits

Introduction into intelligent systems theory, uncertain and incomplete information processing, intelligent systems modeling, model based development of intelligent systems, introduction to softcomputing, agent and multiagent architectures, learning and adaptive systems, reinforcement learning, planing and scheduling, multiagent systems and their applications,  robotic systems, perception, sensor systems, expert systems, datamining.

Guarantor

Language of instruction

Czech, English

Completion

Examination (written)

Time span

39 hrs lectures, 13 hrs projects

Assessment points

60 exam, 20 half-term test, 20 projects

Department

Lecturer

Subject specific learning outcomes and competences

Students acquire knowledge of principles of intelligent systems and so they will be able to design and construct such systems.

Learning objectives

To acquaint students with theory and principles of intelligent systems and with representative practical systems.

Prerequisite kwnowledge and skills

Artificial intelligence basics: Problem solving, state space search, problem decomposition, machine learning principles, statistical and structural pattern recognition. Fundamentals of computer vision. Base principles of natural language processing.

Study literature

  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Negnevitsky, M.: Artificial Intelligence, Addison Wesley, 2001, ISBN 0-321-20466-2

Fundamental literature

  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Negnevitsky, M.: Artificial Intelligence, Addison Wesley, 2001, ISBN 0-321-20466-2

Syllabus of lectures

  1. Introduction
  2. Intelligent systems modeling
  3. Model based development of intelligent systems
  4. Introduction to softcomputing
  5. Agent and multiagent architectures 
  6. Learning and adaptive systems
  7. Reinforcement learning
  8. Planing and Scheduling 
  9. Multiagent systems and their applications
  10. Robotic systems
  11. Perception, sensor systems
  12. Expert systems, datamining
  13. Summary

Syllabus - others, projects and individual work of students

Individual project - design of simple intelligent system

Progress assessment

  • Mid-term written test
  • Individuální project
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