Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2018/2019

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.

Language of instruction

Czech, English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

  • 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.

  • 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.

Prerequisites

  • Basic knowledge of the programming.
  • 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

  • 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.

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

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

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

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
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

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

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

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. 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.

Exercise in computer lab

13 hod., compulsory

Teacher / Lecturer

Ing. Jaroslav Rozman, Ph.D.
Ing. Veronika Šoková
Ing. Jan Tinka
Ing. Václav Uhlíř
Ing. Martin Šůstek
Ing. Štefan Martiček

Syllabus

  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.