Faculty of Information Technology, BUT

Course details

Fundamentals of Artificial Intelligence

IZU Acad. year 2018/2019 Summer semester 4 credits

Current academic year

Problem solving: State space search (BFS, DFS, DLS, IDS, BS, UCS, Backtracking, Forward checking, Min-conflict, BestFS, GS, A*, Hill Climbing, Simulated Annealing methods). Problem decomposition (AND/OR graphs). Solving optimization problems by nature-inspired algorithms (GA, ACO and PSO). Games playing (Mini-Max and Alfa-Beta algorithms). Logic and artificial intelligence (method of resolution and its utilization for task solving and planning). PROLOG language and implementations of basic search algorithms in this language. Machine learning principles. Classification and patterns recognition. Basic principles of expert systems. Fundamentals of computer vision.  Principles of natural language processing. Introduction into agent systems.

Guarantor

Deputy Guarantor

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 13 hrs pc labs

Assessment points

60 exam, 20 half-term test, 20 exercises

Department

Lecturer

Instructor

Martiček Štefan, Ing. (DITS FIT BUT)
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT)
Šoková Veronika, Ing. (DITS FIT BUT)
Šůstek Martin, Ing. (DITS FIT BUT)
Tinka Jan, Ing. (DITS FIT BUT)
Uhlíř Václav, Ing. et Ing. (DITS FIT BUT)

Subject specific learning outcomes and competences

  • Students will learn terminology in Artificial Intelligence field both in Czech and in English language.
  • Students will learn read and so partly write programs in PROLOG language.

Generic learning outcomes and competences

  • Students will acquaint with problem solving methods based on state space search and on decomposition problem into sub-problems.
  • Students will acquaint with basic game playing methods.
  • Students will learn to solve optimization problems.
  • Students will acquaint with fundamentals of propositional and predicate logics and with their applications.
  • Students will learn how to use basic methods of machine learning, classification and recognition.
  • Students will acquaint with fundamentals of expert systems, machine vision and natural language processing.
  • Students will acquaint with fundamentals of multiagent systems.

Learning objectives

To give the students the knowledge of fundamentals of artificial intelligence, namely knowledge of problem solving approaches, machine learning principles and general theory of recognition. Students acquire base information about expert systems, computer vision and natural language processing.

Why is the course taught

In the IZU course, students acquire basic knowledge of artificial intelligence and realize that artificial intelligence does not mean an artificial entity, but that it is a serious and very useful branch of computer science. Students will learn the basics of machine learning and problem solving approaches, which can then be used to design and create artificial intelligent systems.

Prerequisite kwnowledge and skills

  • Basic knowledge of the programming.
  • Knowledge of secondary school level mathematics.

Study literature

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7
  • Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310

Fundamental literature

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7
  • Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
  • Pool, D. L., Mackworth, A. K.: Artificial Intelligence, Cambridge University Press, 2010,  ISBN-13 978-0-521-51900-7

Syllabus of lectures

  1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
  2. State space search methods.
  3. Solving methods using decomposition problems into sub-problems.
  4. Solving optimization problems using algorithms inspired by nature.
  5. Basic methods of game playing.
  6. Logic and AI, resolution and it's application in problem solving and planning.
  7. PROLOG language and its use in AI.
  8. Machine learning.  
  9. Classification and pattern recognition.
  10. Principles of expert systems.
  11. Principles of computer vision.
  12. Principles of natural language processing.
  13. Introduction into agent systems.

Syllabus of computer exercises

  1. Problem solving - State Space Search.
  2. Problem solving - CSP.
  3. Problem solving - game playing.
  4. Predicate logic - method of resolution.
  5. PROLOG language - basic information.
  6. PROLOG language - simple individual programs.
  7. Simple programs for pattern recognition.

Progress assessment

  • Mid-term written examination - 20 points.
  • Programs in computer exercises - 20 points.
  • Final written examination - 60 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.

Controlled instruction

Missed lessons (exercises and tests) can be substituted only exceptionally, after proving that the absences had legitimate reasons.

Exam prerequisites

At least 15 points earned during semester (mid-term test + tasks in computer exercises).

Course inclusion in study plans

  • Programme BIT, 2nd year of study, Compulsory
  • Programme IT-BC-3, field BIT, 2nd year of study, Compulsory
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