|
|
Main menu for Browse IS/STAG
Course info
FES / DTI2
:
Course description
Department/Unit / Abbreviation
|
FES
/
DTI2
|
Academic Year
|
2023/2024
|
Academic Year
|
2023/2024
|
Title
|
Theoretical Informatics - Part II
|
Form of course completion
|
Examination
|
Form of course completion
|
Examination
|
Accredited / Credits
|
Yes,
15
Cred.
|
Type of completion
|
Combined
|
Type of completion
|
Combined
|
Time requirements
|
Lecture
15
[HRS/WEEK]
|
Course credit prior to examination
|
No
|
Course credit prior to examination
|
No
|
Automatic acceptance of credit before examination
|
No
|
Included in study average
|
NO
|
Language of instruction
|
Czech
|
Occ/max
|
|
|
|
Automatic acceptance of credit before examination
|
No
|
Summer semester
|
0 / -
|
0 / -
|
0 / -
|
Included in study average
|
NO
|
Winter semester
|
0 / -
|
0 / -
|
0 / -
|
Repeated registration
|
NO
|
Repeated registration
|
NO
|
Timetable
|
Yes
|
Semester taught
|
Winter + Summer
|
Semester taught
|
Winter + Summer
|
Minimum (B + C) students
|
not determined
|
Optional course |
Yes
|
Optional course
|
Yes
|
Language of instruction
|
Czech
|
Internship duration
|
0
|
No. of hours of on-premise lessons |
|
Evaluation scale |
S|N |
Periodicity |
každý rok
|
Periodicita upřesnění |
|
Fundamental theoretical course |
No
|
Fundamental course |
No
|
Fundamental theoretical course |
No
|
Evaluation scale |
S|N |
Substituted course
|
None
|
Preclusive courses
|
N/A
|
Prerequisite courses
|
N/A
|
Informally recommended courses
|
N/A
|
Courses depending on this Course
|
N/A
|
Histogram of students' grades over the years:
Graphic PNG
,
XLS
|
Course objectives:
|
Cílem předmětu je seznámit studenty doktorského stupně studia s vybranými oblastmi teoretické informatiky v rozsahu potřebném pro kvalifikovanou vědeckou práci v oblasti aplikované informatiky. Celkové pojetí předmětu musí nezbytně odrážet i skutečnost, že ne všichni studenti na předcházejících stupních studia dosáhli stejné úrovně vědomostí v teoretické informatice a výuka musí být organizována tak, aby se případné diference co nejvíce vyrovnaly.
|
Requirements on student
|
Completion and successful defense of project from the field of dissertation work.
|
Content
|
Artificial and computational intelligence. Synthesis and analysis of decision-making processes with uncertainty. Classification and prediction economic processes by fuzzy inference systems. Fuzzy inference system Mamdani. Fuzzy inference system Takagi-Sugeno. Models of neural networks, classification and prediction. Process learning in neural networks. Evolution stochastic optimization algorithms. Neuro-fuzzy-genetic systems. Computational intelligence in decision-making, control, classification and prediction. Ambient intelligence.
|
Activities
|
|
Fields of study
|
|
Guarantors and lecturers
|
|
Literature
|
-
Basic:
KVASNIČKA V. a kol. Evolučné algoritmy.. STU, Bratislava, 2000.
-
Basic:
KUNCHEVA L. I. Fuzzy Classifier Design.. A Springer Verlag Company, Germany, 2000.
-
Basic:
Kvasnička V. a kol. Úvod do teórie neurónových sietí. 1997, IRIS Bratislava.. IRIS, Bratislava, 1997.
-
Recommended:
GHOSH A., TSUTSUI S. Advances in Evolutionary Computing. Theory and Applications.. A Springer-Verlag Company, Germany, 2003.
-
Recommended:
RUTKOWSKI L., KACPRZYK J. Advances in Soft Computing. Neural Networks and Soft Computing.. A Springer-Verlag Company, Germany, 2003.
-
Recommended:
RUSSEL, S.-NORVIG, P. Artificial Intelligence. A Modern Approach. Prentice Hall. New Jersey, 2003.
-
Recommended:
OLEJ V. Modelovanie ekonomických procesov na báze výpočtovej inteligencie.. Miloš Vognar - M&V, Hradec Králové, 2003. ISBN 80-903024-9-1.
|
Prerequisites - other information about course preconditions |
- |
Competences acquired |
The students should be able to design fuzzy inference systems for classification and prediction, especially in the economics, social and environmental spheres, as well as to design models based on neural and fuzzy neural networks. |
Teaching methods |
- Methods of individual activities
|
Assessment methods |
|
|
|
|