Course details

Fundamentals of Artificial Intelligence

IZU Acad. year 2020/2021 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). Solving optimization problems by nature-inspired algorithms (GA, ACO and PSO). Problem decomposition (And Or graphs), games playing (Mini-Max and Alfa-Beta algorithms). AI language PROLOG and implementations of basic search algorithms in this language. Machine learning principles. Statistical and structural pattern recognition. Basic principles of expert systems. Fundamentals of computer vision. Base principles of natural language processing. Application fields of artificial intelligence.

Guarantor

Zbořil František, doc. Ing., Ph.D. (DITS)

Course coordinator

Zbořil František, doc. Ing., CSc. (DITS)

Language of instruction

Czech, English

Completion

Credit+Examination (written)

Time span

  • 26 hrs lectures
  • 13 hrs projects

Assessment points

  • 70 pts final exam (written part)
  • 30 pts projects

Department

Department of Intelligent Systems (UITS)

Lecturer

Kočí Radek, Ing., Ph.D. (DITS)
Zbořil František, doc. Ing., CSc. (DITS)
Zbořil František, doc. Ing., Ph.D. (DITS)

Instructor

Kočí Radek, Ing., Ph.D. (DITS)
Rozman Jaroslav, Ing., Ph.D. (DITS)
Zbořil František, doc. Ing., CSc. (DITS)
Zbořil František, doc. Ing., Ph.D. (DITS)

Course Web Pages

<A HREF="http://www.fit.vutbr.cz/study/courses/IZU/private">www.fit.vutbr.cz/study/courses/IZU/private

Subject specific learning outcomes and competences

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


  • 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 of two players.
  • Students will learn to solve optimization problems.
  • Students will acquaint with fundamentals of propositional and predicate logic and with their applications.
  • Students will learn how to use basic methods of machine learning.
  • 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 should gain knowledge what artificial intelligence is, realize that the artificial intelligence does not mean artificial being, but that it is a serious and very useful branch of computer science. Furthermore, students will learn basic techniques and approaches to solving problems that they can use them for the creation of artificially intelligent systems.

Prerequisite knowledge and skills

  • Basic knowledge of programming in any procedural programming language.
  • Knowledge of secondary school level mathematics.

Study literature

  • Russell,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

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. Methods of game playing (two players).
  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. Pattern recognition.
  10. Principles of expert systems.
  11. Principles of computer vision.
  12. Principles of natural language processing.
  13. Introduction to agent systems.

Syllabus - others, projects and individual work of students

  • Project dealing with state space search and game playing
  • Project dealing with logic and PROLOG language
  • Projects dealing with machine learning 

Progress assessment

  • Projects (homeworks) - 30 points.
  • Final written examination - 70 points; The minimal number of points which can be obtained from the final written examination is 28. Otherwise, no points will be assigned to a student.


Exam prerequisites:
At least one point per project. At least ten points from all projects together.

Controlled instruction

Four projects during the semester.

Exam prerequisites

At least one point per project. At least ten points from all projects together.

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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