Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2020/2021

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.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, Ph.D.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

  • 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.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • 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.

Course curriculum

Not applicable.

Work placements

Not applicable.

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.

Specification of controlled education, way of implementation and compensation for absences

Four projects during the semester.

Recommended optional programme components

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
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

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 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

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

Project

13 hod., compulsory

Teacher / Lecturer

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

Syllabus

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