Course detail

Sources of Artificial Intelligence

FSI-SPUAcad. year: 2011/2012

The course provides students with the introduction to basic resources of artificial intelligence usable in practical applications. The emphasis is put on mechanisms of reasoning, searching and learning. The applicability of introduced resources to engineering problems solving is discussed.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. RNDr. Ing. Tomáš Březina, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

Students will have a clear idea of the adequacy of the use of basic means of artificial intelligence for engineering problems solving.

Prerequisites

The knowledge of basic relations from graphs theory, probability theory and statistics is supposed.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

To pass the course a paper on the theme “Application of engaged artificial intelligence resource on special engineering area” in the scope of about 10 pages must be submitted.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The course objective is to make students familiar with basic means of artificial intelligence, and potential and adequacy of their use in engineering problems solving.

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

The attendance at lectures is obligatory. Education runs according to week schedules. The form of compensation of missed lectures is fully in the competence of a tutor.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kim W.Tracy, Peter Bouthoorn: Object-oriented Artificial Intelligence Using C++.
Edward A. Bender: Mathematical Methods in Artificial Intelligence.
Stuart Russel, Peter Norvig: Artificial Intelligence. A Modern Approach.

Recommended reading

F.Zbořil a kol.: Umělá inteligence (skriptum VUT).
V. Mařík a kol.: Umělá inteligence.

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MAI , 2. year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. RNDr. Ing. Tomáš Březina, CSc.

Syllabus

1. Introduction, AI areas.
2. Trees and search.
3. Heuristic and partial search. Alfa-Beta pruning.
4. Predicate logic, syntax and semantics.
5. Generalized resolution. Prolog.
6. Non-monotonic reasoning. Rule-based systems, semantic nets.
7. Bayesian networks.
8. Machine learning.
9. Decision trees. Rules extraction.
10. Markov models and learning. Q-learning.
11. Neural networks and minimization. Forward and recurrent networks.
12. Genetic algorithms and optimization. Escape from local minimum.
13. Actual state of AI, prospects.