Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2024/2025

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.

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.

Equiment (freely available)

  • SWI PROLOG - verze 6.2.6, Copyright (c) 1990-2012 University of Amsterdam, VU Amsterdam

Exam prerequisites
To be allowed to take the final exam, the student has to obtain at least

  • 10 points from projects and
  • 8 poits from the mid-semester exam

Language of instruction

Czech, English

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, Ph.D.

Department

Department of Intelligent Systems (UITS)

Entry knowledge

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

Rules for evaluation and completion of the course

  • Mid-term written examination - 20 points.
  • Projects (homeworks) - 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.

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

Aims

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.
  • 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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

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
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

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BIT Bachelor's 2 year of study, summer semester, compulsory
  • Programme BIT Bachelor's 2 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Radek Kočí, Ph.D.
doc. Ing. František Zbořil, Ph.D.
doc. Ing. František Zbořil, CSc.

Syllabus

  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. Methods of game playing (two players).
  5. Logic and AI, resolution and it's application in problem-solving and planning.
  6. PROLOG language and its use in AI.
  7. Solving optimization problems using algorithms inspired by nature
  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. 

Project

13 hod., optionally

Teacher / Lecturer

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

Syllabus

  1. Project dealing with state space search and game playing
  2. Project dealing with logic and PROLOG language
  3. Two projects dealing with machine learning and classifiers

Elearning

eLearning: currently opened course